Methods and systems for agent prioritization

ABSTRACT

Provided are methods for agent prioritization, which can include determining a primary agent set and generating, based on the primary agent set, a trajectory for the autonomous vehicle. Some methods described also include determining an interaction parameter of agents in the environment. Systems and computer program products are also provided.

TECHNICAL FIELD

The present disclosure relates generally to methods and systems foroperating an autonomous vehicle.

BACKGROUND

Autonomous vehicles can use a number of methods and systems fordetermining a trajectory for the autonomous vehicle. However, thesemethods and systems can require high computational power, which can leadto inefficient computation. Further, the methods and systems can slowthe reaction time of the autonomous vehicle, which can lead toreal-world complications.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is an example environment in which a vehicle including one ormore components of an autonomous system can be implemented;

FIG. 2 is a diagram of one or more systems of a vehicle including anautonomous system;

FIG. 3 is a diagram of components of one or more devices and/or one ormore systems of FIGS. 1 and 2 ;

FIG. 4 is a diagram of certain components of an autonomous system;

FIGS. 5A-5C are diagrams of an implementation of an example process foragent prioritization;

FIG. 6 is a diagram of training procedures for an example implementationof a process for agent prioritization;

FIGS. 7A-7B are diagrams of an example implementation of a process foragent prioritization;

FIG. 8 illustrates a flowchart of an example process for agentprioritization; and

FIG. 9 is a diagram of computational efficiency data including a processfor agent prioritization.

DETAILED DESCRIPTION

In the following description numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure forthe purposes of explanation. It will be apparent, however, that theembodiments described by the present disclosure can be practiced withoutthese specific details. In some instances, well-known structures anddevices are illustrated in block diagram form in order to avoidunnecessarily obscuring aspects of the present disclosure.

Specific arrangements or orderings of schematic elements, such as thoserepresenting systems, devices, modules, instruction blocks, dataelements, and/or the like are illustrated in the drawings for ease ofdescription. However, it will be understood by those skilled in the artthat the specific ordering or arrangement of the schematic elements inthe drawings is not meant to imply that a particular order or sequenceof processing, or separation of processes, is required unless explicitlydescribed as such. Further, the inclusion of a schematic element in adrawing is not meant to imply that such element is required in allembodiments or that the features represented by such element may not beincluded in or combined with other elements in some embodiments unlessexplicitly described as such.

Further, where connecting elements such as solid or dashed lines orarrows are used in the drawings to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not illustrated in the drawings so as not toobscure the disclosure. In addition, for ease of illustration, a singleconnecting element can be used to represent multiple connections,relationships or associations between elements. For example, where aconnecting element represents communication of signals, data, orinstructions (e.g., “software instructions”), it should be understood bythose skilled in the art that such element can represent one or multiplesignal paths (e.g., a bus), as may be needed, to affect thecommunication.

Although the terms first, second, third, and/or the like are used todescribe various elements, these elements should not be limited by theseterms. The terms first, second, third, and/or the like are used only todistinguish one element from another. For example, a first contact couldbe termed a second contact and, similarly, a second contact could betermed a first contact without departing from the scope of the describedembodiments. The first contact and the second contact are both contacts,but they are not the same contact.

The terminology used in the description of the various describedembodiments herein is included for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well and can be used interchangeably with “one ormore” or “at least one,” unless the context clearly indicates otherwise.It will also be understood that the term “and/or” as used herein refersto and encompasses any and all possible combinations of one or more ofthe associated listed items. It will be further understood that theterms “includes,” “including,” “comprises,” and/or “comprising,” whenused in this description specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the terms “communication” and “communicate” refer to atleast one of the reception, receipt, transmission, transfer, provision,and/or the like of information (or information represented by, forexample, data, signals, messages, instructions, commands, and/or thelike). For one unit (e.g., a device, a system, a component of a deviceor system, combinations thereof, and/or the like) to be in communicationwith another unit means that the one unit is able to directly orindirectly receive information from and/or send (e.g., transmit)information to the other unit. This may refer to a direct or indirectconnection that is wired and/or wireless in nature. Additionally, twounits may be in communication with each other even though theinformation transmitted may be modified, processed, relayed, and/orrouted between the first and second unit. For example, a first unit maybe in communication with a second unit even though the first unitpassively receives information and does not actively transmitinformation to the second unit. As another example, a first unit may bein communication with a second unit if at least one intermediary unit(e.g., a third unit located between the first unit and the second unit)processes information received from the first unit and transmits theprocessed information to the second unit. In some embodiments, a messagemay refer to a network packet (e.g., a data packet and/or the like) thatincludes data.

As used herein, the term “if” is, optionally, construed to mean “when”,“upon”, “in response to determining,” “in response to detecting,” and/orthe like, depending on the context. Similarly, the phrase “if it isdetermined” or “if [a stated condition or event] is detected” is,optionally, construed to mean “upon determining,” “in response todetermining,” “upon detecting [the stated condition or event],” “inresponse to detecting [the stated condition or event],” and/or the like,depending on the context. Also, as used herein, the terms “has”, “have”,“having”, or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based at least partially on”unless explicitly stated otherwise.

“At least one,” and “one or more” includes a function being performed byone element, a function being performed by more than one element, e.g.,in a distributed fashion, several functions being performed by oneelement, several functions being performed by several elements, or anycombination of the above.

Some embodiments of the present disclosure are described herein inconnection with a threshold. As described herein, satisfying or meetinga threshold can refer to a value being greater than the threshold, morethan the threshold, higher than the threshold, greater than or equal tothe threshold, less than the threshold, fewer than the threshold, lowerthan the threshold, less than or equal to the threshold, equal to thethreshold, and/or the like.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments can be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

General Overview

In some aspects and/or embodiments, systems, methods, and computerprogram products described herein include and/or implement a method foroperating an autonomous vehicle. Specifically, described herein aresystems, methods, and computer program products for agent prioritizationby using one or more interaction parameters predicative of interactionof the autonomous vehicle with a plurality of agents located in anenvironment that the autonomous vehicle is located in. Advantageously,the systems, methods, and computer program products described herein canapply prioritization and/or filtration.

By virtue of the implementation of systems, methods, and computerprogram products described herein, techniques for agent prioritizationcan improve computational efficiency, such as for providing trajectoriesfor the autonomous vehicle. For example, the systems, methods, andcomputer program products described herein can lead to reducedcomputational times and/or modified computational fidelity. This may beadvantageous for real-time operation of the autonomous vehicle.

Referring now to FIG. 1 , illustrated is example environment 100 inwhich vehicles that include autonomous systems, as well as vehicles thatdo not, are operated. As illustrated, environment 100 includes vehicles102 a-102 n, objects 104 a-104 n, routes 106 a-106 n, area 108,vehicle-to-infrastructure (V2I) device 110, network 112, remoteautonomous vehicle (AV) system 114, fleet management system 116, and V2Isystem 118. Vehicles 102 a-102 n, vehicle-to-infrastructure (V2I) device110, network 112, autonomous vehicle (AV) system 114, fleet managementsystem 116, and V2I system 118 interconnect (e.g., establish aconnection to communicate and/or the like) via wired connections,wireless connections, or a combination of wired or wireless connections.In some embodiments, objects 104 a-104 n interconnect with at least oneof vehicles 102 a-102 n, vehicle-to-infrastructure (V2I) device 110,network 112, autonomous vehicle (AV) system 114, fleet management system116, and V2I system 118 via wired connections, wireless connections, ora combination of wired or wireless connections.

Vehicles 102 a-102 n (referred to individually as vehicle 102 andcollectively as vehicles 102) include at least one device configured totransport goods and/or people. In some embodiments, vehicles 102 areconfigured to be in communication with V2I device 110, remote AV system114, fleet management system 116, and/or V2I system 118 via network 112.In some embodiments, vehicles 102 include cars, buses, trucks, trains,and/or the like. In some embodiments, vehicles 102 are the same as, orsimilar to, vehicles 200, described herein (see FIG. 2 ). In someembodiments, a vehicle 200 of a set of vehicles 200 is associated withan autonomous fleet manager. In some embodiments, vehicles 102 travelalong respective routes 106 a-106 n (referred to individually as route106 and collectively as routes 106), as described herein. In someembodiments, one or more vehicles 102 include an autonomous system(e.g., an autonomous system that is the same as or similar to autonomoussystem 202).

Objects 104 a-104 n (referred to individually as object 104 andcollectively as objects 104) include, for example, at least one vehicle,at least one pedestrian, at least one cyclist, at least one structure(e.g., a building, a sign, a fire hydrant, etc.), and/or the like. Eachobject 104 is stationary (e.g., located at a fixed location for a periodof time) or mobile (e.g., having a velocity and associated with at leastone trajectory). In some embodiments, objects 104 are associated withcorresponding locations in area 108.

Routes 106 a-106 n (referred to individually as route 106 andcollectively as routes 106) are each associated with (e.g., prescribe) asequence of actions (also known as a trajectory) connecting states alongwhich an AV can navigate. Each route 106 starts at an initial state(e.g., a state that corresponds to a first spatiotemporal location,velocity, and/or the like) and ends at a final goal state (e.g., a statethat corresponds to a second spatiotemporal location that is differentfrom the first spatiotemporal location) or goal region (e.g. a subspaceof acceptable states (e.g., terminal states)). In some embodiments, thefirst state includes a location at which an individual or individualsare to be picked-up by the AV and the second state or region includes alocation or locations at which the individual or individuals picked-upby the AV are to be dropped-off. In some embodiments, routes 106 includea plurality of acceptable state sequences (e.g., a plurality ofspatiotemporal location sequences), the plurality of state sequencesassociated with (e.g., defining) a plurality of trajectories. In anexample, routes 106 include only high level actions or imprecise statelocations, such as a series of connected roads dictating turningdirections at roadway intersections. Additionally, or alternatively,routes 106 may include more precise actions or states such as, forexample, specific target lanes or precise locations within the laneareas and targeted speed at those positions. In an example, routes 106include a plurality of precise state sequences along the at least onehigh level action sequence with a limited lookahead horizon to reachintermediate goals, where the combination of successive iterations oflimited horizon state sequences cumulatively correspond to a pluralityof trajectories that collectively form the high level route to terminateat the final goal state or region.

Area 108 includes a physical area (e.g., a geographic region) withinwhich vehicles 102 can navigate. In an example, area 108 includes atleast one state (e.g., a country, a province, an individual state of aplurality of states included in a country, etc.), at least one portionof a state, at least one city, at least one portion of a city, etc. Insome embodiments, area 108 includes at least one named thoroughfare(referred to herein as a “road”) such as a highway, an interstatehighway, a parkway, a city street, etc. Additionally, or alternatively,in some examples area 108 includes at least one unnamed road such as adriveway, a section of a parking lot, a section of a vacant and/orundeveloped lot, a dirt path, etc. In some embodiments, a road includesat least one lane (e.g., a portion of the road that can be traversed byvehicles 102). In an example, a road includes at least one laneassociated with (e.g., identified based on) at least one lane marking.

Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as aVehicle-to-Infrastructure or Vehicle-to-Everything (V2X) device)includes at least one device configured to be in communication withvehicles 102 and/or V2I infrastructure system 118. In some embodiments,V2I device 110 is configured to be in communication with vehicles 102,remote AV system 114, fleet management system 116, and/or V2I system 118via network 112. In some embodiments, V2I device 110 includes a radiofrequency identification (RFID) device, signage, cameras (e.g.,two-dimensional (2D) and/or three-dimensional (3D) cameras), lanemarkers, streetlights, parking meters, etc. In some embodiments, V2Idevice 110 is configured to communicate directly with vehicles 102.Additionally, or alternatively, in some embodiments V2I device 110 isconfigured to communicate with vehicles 102, remote AV system 114,and/or fleet management system 116 via V2I system 118. In someembodiments, V2I device 110 is configured to communicate with V2I system118 via network 112.

Network 112 includes one or more wired and/or wireless networks. In anexample, network 112 includes a cellular network (e.g., a long termevolution (LTE) network, a third generation (3G) network, a fourthgeneration (4G) network, a fifth generation (5G) network, a codedivision multiple access (CDMA) network, etc.), a public land mobilenetwork (PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the publicswitched telephone network (PSTN), a private network, an ad hoc network,an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, etc., a combination of some or all of these networks,and/or the like.

Remote AV system 114 includes at least one device configured to be incommunication with vehicles 102, V2I device 110, network 112, fleetmanagement system 116, and/or V2I system 118 via network 112. In anexample, remote AV system 114 includes a server, a group of servers,and/or other like devices. In some embodiments, remote AV system 114 isco-located with the fleet management system 116. In some embodiments,remote AV system 114 is involved in the installation of some or all ofthe components of a vehicle, including an autonomous system, anautonomous vehicle compute, software implemented by an autonomousvehicle compute, and/or the like. In some embodiments, remote AV system114 maintains (e.g., updates and/or replaces) such components and/orsoftware during the lifetime of the vehicle.

Fleet management system 116 includes at least one device configured tobe in communication with vehicles 102, V2I device 110, remote AV system114, and/or V2I infrastructure system 118. In an example, fleetmanagement system 116 includes a server, a group of servers, and/orother like devices. In some embodiments, fleet management system 116 isassociated with a ridesharing company (e.g., an organization thatcontrols operation of multiple vehicles (e.g., vehicles that includeautonomous systems and/or vehicles that do not include autonomoussystems) and/or the like).

In some embodiments, V2I system 118 includes at least one deviceconfigured to be in communication with vehicles 102, V2I device 110,remote AV system 114, and/or fleet management system 116 via network112. In some examples, V2I system 118 is configured to be incommunication with V2I device 110 via a connection different fromnetwork 112. In some embodiments, V2I system 118 includes a server, agroup of servers, and/or other like devices. In some embodiments, V2Isystem 118 is associated with a municipality or a private institution(e.g., a private institution that maintains V2I device 110 and/or thelike).

The number and arrangement of elements illustrated in FIG. 1 areprovided as an example. There can be additional elements, fewerelements, different elements, and/or differently arranged elements, thanthose illustrated in FIG. 1 . Additionally, or alternatively, at leastone element of environment 100 can perform one or more functionsdescribed as being performed by at least one different element of FIG. 1. Additionally, or alternatively, at least one set of elements ofenvironment 100 can perform one or more functions described as beingperformed by at least one different set of elements of environment 100.

Referring now to FIG. 2 , vehicle 200 includes autonomous system 202,powertrain control system 204, steering control system 206, and brakesystem 208. In some embodiments, vehicle 200 is the same as or similarto vehicle 102 (see FIG. 1 ). In some embodiments, vehicle 200 hasautonomous capability (e.g., implement at least one function, feature,device, and/or the like that enable vehicle 200 to be partially or fullyoperated without human intervention including, without limitation, fullyautonomous vehicles (e.g., vehicles that forego reliance on humanintervention), highly autonomous vehicles (e.g., vehicles that foregoreliance on human intervention in certain situations), and/or the like).For a detailed description of fully autonomous vehicles and highlyautonomous vehicles, reference may be made to SAE International'sstandard J3016: Taxonomy and Definitions for Terms Related to On-RoadMotor Vehicle Automated Driving Systems, which is incorporated byreference in its entirety. In some embodiments, vehicle 200 isassociated with an autonomous fleet manager and/or a ridesharingcompany.

Autonomous system 202 includes a sensor suite that includes one or moredevices such as cameras 202 a, LiDAR sensors 202 b, radar sensors 202 c,and microphones 202 d. In some embodiments, autonomous system 202 caninclude more or fewer devices and/or different devices (e.g., ultrasonicsensors, inertial sensors, GPS receivers (discussed below), odometrysensors that generate data associated with an indication of a distancethat vehicle 200 has traveled, and/or the like). In some embodiments,autonomous system 202 uses the one or more devices included inautonomous system 202 to generate data associated with environment 100,described herein. The data generated by the one or more devices ofautonomous system 202 can be used by one or more systems describedherein to observe the environment (e.g., environment 100) in whichvehicle 200 is located. In some embodiments, autonomous system 202includes communication device 202 e, autonomous vehicle compute 202 f,and safety controller 202 g.

Cameras 202 a include at least one device configured to be incommunication with communication device 202 e, autonomous vehiclecompute 202 f, and/or safety controller 202 g via a bus (e.g., a busthat is the same as or similar to bus 302 of FIG. 3 ). Cameras 202 ainclude at least one camera (e.g., a digital camera using a light sensorsuch as a charge-coupled device (CCD), a thermal camera, an infrared(IR) camera, an event camera, and/or the like) to capture imagesincluding physical objects (e.g., cars, buses, curbs, people, and/or thelike). In some embodiments, camera 202 a generates camera data asoutput. In some examples, camera 202 a generates camera data thatincludes image data associated with an image. In this example, the imagedata may specify at least one parameter (e.g., image characteristicssuch as exposure, brightness, etc., an image timestamp, and/or the like)corresponding to the image. In such an example, the image may be in aformat (e.g., RAW, JPEG, PNG, and/or the like). In some embodiments,camera 202 a includes a plurality of independent cameras configured on(e.g., positioned on) a vehicle to capture images for the purpose ofstereopsis (stereo vision). In some examples, camera 202 a includes aplurality of cameras that generate image data and transmit the imagedata to autonomous vehicle compute 202 f and/or a fleet managementsystem (e.g., a fleet management system that is the same as or similarto fleet management system 116 of FIG. 1 ). In such an example,autonomous vehicle compute 202 f determines depth to one or more objectsin a field of view of at least two cameras of the plurality of camerasbased on the image data from the at least two cameras. In someembodiments, cameras 202 a is configured to capture images of objectswithin a distance from cameras 202 a (e.g., up to 100 meters, up to akilometer, and/or the like). Accordingly, cameras 202 a include featuressuch as sensors and lenses that are optimized for perceiving objectsthat are at one or more distances from cameras 202 a.

In an embodiment, camera 202 a includes at least one camera configuredto capture one or more images associated with one or more trafficlights, street signs and/or other physical objects that provide visualnavigation information. In some embodiments, camera 202 a generatestraffic light data associated with one or more images. In some examples,camera 202 a generates TLD data associated with one or more images thatinclude a format (e.g., RAW, JPEG, PNG, and/or the like). In someembodiments, camera 202 a that generates TLD data differs from othersystems described herein incorporating cameras in that camera 202 a caninclude one or more cameras with a wide field of view (e.g., awide-angle lens, a fish-eye lens, a lens having a viewing angle ofapproximately 120 degrees or more, and/or the like) to generate imagesabout as many physical objects as possible.

Laser Detection and Ranging (LiDAR) sensors 202 b include at least onedevice configured to be in communication with communication device 202e, autonomous vehicle compute 202 f, and/or safety controller 202 g viaa bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). LiDAR sensors 202 b include a system configured to transmit lightfrom a light emitter (e.g., a laser transmitter). Light emitted by LiDARsensors 202 b include light (e.g., infrared light and/or the like) thatis outside of the visible spectrum. In some embodiments, duringoperation, light emitted by LiDAR sensors 202 b encounters a physicalobject (e.g., a vehicle) and is reflected back to LiDAR sensors 202 b.In some embodiments, the light emitted by LiDAR sensors 202 b does notpenetrate the physical objects that the light encounters. LiDAR sensors202 b also include at least one light detector which detects the lightthat was emitted from the light emitter after the light encounters aphysical object. In some embodiments, at least one data processingsystem associated with LiDAR sensors 202 b generates an image (e.g., apoint cloud, a combined point cloud, and/or the like) representing theobjects included in a field of view of LiDAR sensors 202 b. In someexamples, the at least one data processing system associated with LiDARsensor 202 b generates an image that represents the boundaries of aphysical object, the surfaces (e.g., the topology of the surfaces) ofthe physical object, and/or the like. In such an example, the image isused to determine the boundaries of physical objects in the field ofview of LiDAR sensors 202 b.

Radio Detection and Ranging (radar) sensors 202 c include at least onedevice configured to be in communication with communication device 202e, autonomous vehicle compute 202 f, and/or safety controller 202 g viaa bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Radar sensors 202 c include a system configured to transmit radiowaves (either pulsed or continuously). The radio waves transmitted byradar sensors 202 c include radio waves that are within a predeterminedspectrum In some embodiments, during operation, radio waves transmittedby radar sensors 202 c encounter a physical object and are reflectedback to radar sensors 202 c. In some embodiments, the radio wavestransmitted by radar sensors 202 c are not reflected by some objects. Insome embodiments, at least one data processing system associated withradar sensors 202 c generates signals representing the objects includedin a field of view of radar sensors 202 c. For example, the at least onedata processing system associated with radar sensor 202 c generates animage that represents the boundaries of a physical object, the surfaces(e.g., the topology of the surfaces) of the physical object, and/or thelike. In some examples, the image is used to determine the boundaries ofphysical objects in the field of view of radar sensors 202 c.

Microphones 202 d includes at least one device configured to be incommunication with communication device 202 e, autonomous vehiclecompute 202 f, and/or safety controller 202 g via a bus (e.g., a busthat is the same as or similar to bus 302 of FIG. 3 ). Microphones 202 dinclude one or more microphones (e.g., array microphones, externalmicrophones, and/or the like) that capture audio signals and generatedata associated with (e.g., representing) the audio signals. In someexamples, microphones 202 d include transducer devices and/or likedevices. In some embodiments, one or more systems described herein canreceive the data generated by microphones 202 d and determine a positionof an object relative to vehicle 200 (e.g., a distance and/or the like)based on the audio signals associated with the data.

Communication device 202 e include at least one device configured to bein communication with cameras 202 a, LiDAR sensors 202 b, radar sensors202 c, microphones 202 d, autonomous vehicle compute 202 f, safetycontroller 202 g, and/or DBW system 202 h. For example, communicationdevice 202 e may include a device that is the same as or similar tocommunication interface 314 of FIG. 3 . In some embodiments,communication device 202 e includes a vehicle-to-vehicle (V2V)communication device (e.g., a device that enables wireless communicationof data between vehicles).

Autonomous vehicle compute 202 f include at least one device configuredto be in communication with cameras 202 a, LiDAR sensors 202 b, radarsensors 202 c, microphones 202 d, communication device 202 e, safetycontroller 202 g, and/or DBW system 202 h. In some examples, autonomousvehicle compute 202 f includes a device such as a client device, amobile device (e.g., a cellular telephone, a tablet, and/or the like) aserver (e.g., a computing device including one or more centralprocessing units, graphical processing units, and/or the like), and/orthe like. In some embodiments, autonomous vehicle compute 202 f is thesame as or similar to autonomous vehicle compute 400, described herein.Additionally, or alternatively, in some embodiments autonomous vehiclecompute 202 f is configured to be in communication with an autonomousvehicle system (e.g., an autonomous vehicle system that is the same asor similar to remote AV system 114 of FIG. 1 ), a fleet managementsystem (e.g., a fleet management system that is the same as or similarto fleet management system 116 of FIG. 1 ), a V2I device (e.g., a V2Idevice that is the same as or similar to V2I device 110 of FIG. 1 ),and/or a V2I system (e.g., a V2I system that is the same as or similarto V2I system 118 of FIG. 1 ).

Safety controller 202 g includes at least one device configured to be incommunication with cameras 202 a, LiDAR sensors 202 b, radar sensors 202c, microphones 202 d, communication device 202 e, autonomous vehiclecomputer 202 f, and/or DBW system 202 h. In some examples, safetycontroller 202 g includes one or more controllers (electricalcontrollers, electromechanical controllers, and/or the like) that areconfigured to generate and/or transmit control signals to operate one ormore devices of vehicle 200 (e.g., powertrain control system 204,steering control system 206, brake system 208, and/or the like). In someembodiments, safety controller 202 g is configured to generate controlsignals that take precedence over (e.g., overrides) control signalsgenerated and/or transmitted by autonomous vehicle compute 202 f.

DBW system 202 h includes at least one device configured to be incommunication with communication device 202 e and/or autonomous vehiclecompute 202 f. In some examples, DBW system 202 h includes one or morecontrollers (e.g., electrical controllers, electromechanicalcontrollers, and/or the like) that are configured to generate and/ortransmit control signals to operate one or more devices of vehicle 200(e.g., powertrain control system 204, steering control system 206, brakesystem 208, and/or the like). Additionally, or alternatively, the one ormore controllers of DBW system 202 h are configured to generate and/ortransmit control signals to operate at least one different device (e.g.,a turn signal, headlights, door locks, windshield wipers, and/or thelike) of vehicle 200.

Powertrain control system 204 includes at least one device configured tobe in communication with DBW system 202 h. In some examples, powertraincontrol system 204 includes at least one controller, actuator, and/orthe like. In some embodiments, powertrain control system 204 receivescontrol signals from DBW system 202 h and powertrain control system 204causes vehicle 200 to start moving forward, stop moving forward, startmoving backward, stop moving backward, accelerate in a direction,decelerate in a direction, perform a left turn, perform a right turn,and/or the like. In an example, powertrain control system 204 causes theenergy (e.g., fuel, electricity, and/or the like) provided to a motor ofthe vehicle to increase, remain the same, or decrease, thereby causingat least one wheel of vehicle 200 to rotate or not rotate.

Steering control system 206 includes at least one device configured torotate one or more wheels of vehicle 200. In some examples, steeringcontrol system 206 includes at least one controller, actuator, and/orthe like. In some embodiments, steering control system 206 causes thefront two wheels and/or the rear two wheels of vehicle 200 to rotate tothe left or right to cause vehicle 200 to turn to the left or right.

Brake system 208 includes at least one device configured to actuate oneor more brakes to cause vehicle 200 to reduce speed and/or remainstationary. In some examples, brake system 208 includes at least onecontroller and/or actuator that is configured to cause one or morecalipers associated with one or more wheels of vehicle 200 to close on acorresponding rotor of vehicle 200. Additionally, or alternatively, insome examples brake system 208 includes an automatic emergency braking(AEB) system, a regenerative braking system, and/or the like.

In some embodiments, vehicle 200 includes at least one platform sensor(not explicitly illustrated) that measures or infers properties of astate or a condition of vehicle 200. In some examples, vehicle 200includes platform sensors such as a global positioning system (GPS)receiver, an inertial measurement unit (IMU), a wheel speed sensor, awheel brake pressure sensor, a wheel torque sensor, an engine torquesensor, a steering angle sensor, and/or the like.

Referring now to FIG. 3 , illustrated is a schematic diagram of a device300. As illustrated, device 300 includes processor 304, memory 306,storage component 308, input interface 310, output interface 312,communication interface 314, and bus 302. In some embodiments, device300 corresponds to at least one device of vehicles 102 (e.g., at leastone device of a system of vehicles 102), at least one device of remoteAV system 114, fleet management system 116, V2I system 118, and/or oneor more devices of network 112 (e.g., one or more devices of a system ofnetwork 112). In some embodiments, one or more devices of vehicles 102(e.g., one or more devices of a system of vehicles 102), such as atleast one device of remote AV system 114, fleet management system 116,and V2I system 118, and/or one or more devices of network 112 (e.g., oneor more devices of a system of network 112) include at least one device300 and/or at least one component of device 300. As shown in FIG. 3 ,device 300 includes bus 302, processor 304, memory 306, storagecomponent 308, input interface 310, output interface 312, andcommunication interface 314.

Bus 302 includes a component that permits communication among thecomponents of device 300. In some embodiments, processor 304 isimplemented in hardware, software, or a combination of hardware andsoftware. In some examples, processor 304 includes a processor (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), and/or the like), a microphone, adigital signal processor (DSP), and/or any processing component (e.g., afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), and/or the like) that can be programmed to perform atleast one function. Memory 306 includes random access memory (RAM),read-only memory (ROM), and/or another type of dynamic and/or staticstorage device (e.g., flash memory, magnetic memory, optical memory,and/or the like) that stores data and/or instructions for use byprocessor 304.

Storage component 308 stores data and/or software related to theoperation and use of device 300. In some examples, storage component 308includes a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, a solid state disk, and/or the like), a compact disc(CD), a digital versatile disc (DVD), a floppy disk, a cartridge, amagnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and/oranother type of computer readable medium, along with a correspondingdrive.

Input interface 310 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touchscreendisplay, a keyboard, a keypad, a mouse, a button, a switch, amicrophone, a camera, and/or the like). Additionally or alternatively,in some embodiments input interface 310 includes a sensor that sensesinformation (e.g., a global positioning system (GPS) receiver, anaccelerometer, a gyroscope, an actuator, and/or the like). Outputinterface 312 includes a component that provides output information fromdevice 300 (e.g., a display, a speaker, one or more light-emittingdiodes (LEDs), and/or the like).

In some embodiments, communication interface 314 includes atransceiver-like component (e.g., a transceiver, a separate receiver andtransmitter, and/or the like) that permits device 300 to communicatewith other devices via a wired connection, a wireless connection, or acombination of wired and wireless connections. In some examples,communication interface 314 permits device 300 to receive informationfrom another device and/or provide information to another device. Insome examples, communication interface 314 includes an Ethernetinterface, an optical interface, a coaxial interface, an infraredinterface, a radio frequency (RF) interface, a universal serial bus(USB) interface, a Wi-Fi® interface, a cellular network interface,and/or the like.

In some embodiments, device 300 performs one or more processes describedherein. Device 300 performs these processes based on processor 304executing software instructions stored by a computer-readable medium,such as memory 305 and/or storage component 308. A computer-readablemedium (e.g., a non-transitory computer readable medium) is definedherein as a non-transitory memory device. A non-transitory memory deviceincludes memory space located inside a single physical storage device ormemory space spread across multiple physical storage devices.

In some embodiments, software instructions are read into memory 306and/or storage component 308 from another computer-readable medium orfrom another device via communication interface 314. When executed,software instructions stored in memory 306 and/or storage component 308cause processor 304 to perform one or more processes described herein.Additionally or alternatively, hardwired circuitry is used in place ofor in combination with software instructions to perform one or moreprocesses described herein. Thus, embodiments described herein are notlimited to any specific combination of hardware circuitry and softwareunless explicitly stated otherwise.

Memory 306 and/or storage component 308 includes data storage or atleast one data structure (e.g., a database and/or the like). Device 300is capable of receiving information from, storing information in,communicating information to, or searching information stored in thedata storage or the at least one data structure in memory 306 or storagecomponent 308. In some examples, the information includes network data,input data, output data, or any combination thereof.

In some embodiments, device 300 is configured to execute softwareinstructions that are either stored in memory 306 and/or in the memoryof another device (e.g., another device that is the same as or similarto device 300). As used herein, the term “module” refers to at least oneinstruction stored in memory 306 and/or in the memory of another devicethat, when executed by processor 304 and/or by a processor of anotherdevice (e.g., another device that is the same as or similar to device300) cause device 300 (e.g., at least one component of device 300) toperform one or more processes described herein. In some embodiments, amodule is implemented in software, firmware, hardware, and/or the like.

The number and arrangement of components illustrated in FIG. 3 areprovided as an example. In some embodiments, device 300 can includeadditional components, fewer components, different components, ordifferently arranged components than those illustrated in FIG. 3 .Additionally or alternatively, a set of components (e.g., one or morecomponents) of device 300 can perform one or more functions described asbeing performed by another component or another set of components ofdevice 300.

Referring now to FIG. 4 , illustrated is an example block diagram of anautonomous vehicle compute 400 (sometimes referred to as an “AV stack”).As illustrated, autonomous vehicle compute 400 includes perceptionsystem 402 (sometimes referred to as a perception module), planningsystem 404 (sometimes referred to as a planning module), localizationsystem 406 (sometimes referred to as a localization module), controlsystem 408 (sometimes referred to as a control module), and database410. In some embodiments, perception system 402, planning system 404,localization system 406, control system 408, and database 410 areincluded and/or implemented in an autonomous navigation system of avehicle (e.g., autonomous vehicle compute 202 f of vehicle 200).Additionally, or alternatively, in some embodiments perception system402, planning system 404, localization system 406, control system 408,and database 410 are included in one or more standalone systems (e.g.,one or more systems that are the same as or similar to autonomousvehicle compute 400 and/or the like). In some examples, perceptionsystem 402, planning system 404, localization system 406, control system408, and database 410 are included in one or more standalone systemsthat are located in a vehicle and/or at least one remote system asdescribed herein. In some embodiments, any and/or all of the systemsincluded in autonomous vehicle compute 400 are implemented in software(e.g., in software instructions stored in memory), computer hardware(e.g., by microprocessors, microcontrollers, application-specificintegrated circuits [ASICs], Field Programmable Gate Arrays (FPGAs),and/or the like), or combinations of computer software and computerhardware. It will also be understood that, in some embodiments,autonomous vehicle compute 400 is configured to be in communication witha remote system (e.g., an autonomous vehicle system that is the same asor similar to remote AV system 114, a fleet management system 116 thatis the same as or similar to fleet management system 116, a V2I systemthat is the same as or similar to V2I system 118, and/or the like).

In some embodiments, perception system 402 receives data associated withat least one physical object (e.g., data that is used by perceptionsystem 402 to detect the at least one physical object) in an environmentand classifies the at least one physical object. In some examples,perception system 402 receives image data captured by at least onecamera (e.g., cameras 202 a), the image associated with (e.g.,representing) one or more physical objects within a field of view of theat least one camera. In such an example, perception system 402classifies at least one physical object based on one or more groupingsof physical objects (e.g., bicycles, vehicles, traffic signs,pedestrians, and/or the like). In some embodiments, perception system402 transmits data associated with the classification of the physicalobjects to planning system 404 based on perception system 402classifying the physical objects.

In some embodiments, planning system 404 receives data associated with adestination and generates data associated with at least one route (e.g.,routes 106) along which a vehicle (e.g., vehicles 102) can travel alongtoward a destination. In some embodiments, planning system 404periodically or continuously receives data from perception system 402(e.g., data associated with the classification of physical objects,described above) and planning system 404 updates the at least onetrajectory or generates at least one different trajectory based on thedata generated by perception system 402. In some embodiments, planningsystem 404 receives data associated with an updated position of avehicle (e.g., vehicles 102) from localization system 406 and planningsystem 404 updates the at least one trajectory or generates at least onedifferent trajectory based on the data generated by localization system406.

In some embodiments, localization system 406 receives data associatedwith (e.g., representing) a location of a vehicle (e.g., vehicles 102)in an area. In some examples, localization system 406 receives LiDARdata associated with at least one point cloud generated by at least oneLiDAR sensor (e.g., LiDAR sensors 202 b). In certain examples,localization system 406 receives data associated with at least one pointcloud from multiple LiDAR sensors and localization system 406 generatesa combined point cloud based on each of the point clouds. In theseexamples, localization system 406 compares the at least one point cloudor the combined point cloud to two-dimensional (2D) and/or athree-dimensional (3D) map of the area stored in database 410.Localization system 406 then determines the position of the vehicle inthe area based on localization system 406 comparing the at least onepoint cloud or the combined point cloud to the map. In some embodiments,the map includes a combined point cloud of the area generated prior tonavigation of the vehicle. In some embodiments, maps include, withoutlimitation, high-precision maps of the roadway geometric properties,maps describing road network connectivity properties, maps describingroadway physical properties (such as traffic speed, traffic volume, thenumber of vehicular and cyclist traffic lanes, lane width, lane trafficdirections, or lane marker types and locations, or combinationsthereof), and maps describing the spatial locations of road featuressuch as crosswalks, traffic signs or other travel signals of varioustypes. In some embodiments, the map is generated in real-time based onthe data received by the perception system.

In another example, localization system 406 receives Global NavigationSatellite System (GNSS) data generated by a global positioning system(GPS) receiver. In some examples, localization system 406 receives GNSSdata associated with the location of the vehicle in the area andlocalization system 406 determines a latitude and longitude of thevehicle in the area. In such an example, localization system 406determines the position of the vehicle in the area based on the latitudeand longitude of the vehicle. In some embodiments, localization system406 generates data associated with the position of the vehicle. In someexamples, localization system 406 generates data associated with theposition of the vehicle based on localization system 406 determining theposition of the vehicle. In such an example, the data associated withthe position of the vehicle includes data associated with one or moresemantic properties corresponding to the position of the vehicle.

In some embodiments, control system 408 receives data associated with atleast one trajectory from planning system 404 and control system 408controls operation of the vehicle. In some examples, control system 408receives data associated with at least one trajectory from planningsystem 404 and control system 408 controls operation of the vehicle bygenerating and transmitting control signals to cause a powertraincontrol system (e.g., DBW system 202 h, powertrain control system 204,and/or the like), a steering control system (e.g., steering controlsystem 206), and/or a brake system (e.g., brake system 208) to operate.In an example, where a trajectory includes a left turn, control system408 transmits a control signal to cause steering control system 206 toadjust a steering angle of vehicle 200, thereby causing vehicle 200 toturn left. Additionally, or alternatively, control system 408 generatesand transmits control signals to cause other devices (e.g., headlights,turn signal, door locks, windshield wipers, and/or the like) of vehicle200 to change states.

In some embodiments, perception system 402, planning system 404,localization system 406, and/or control system 408 implement at leastone machine learning model (e.g., at least one multilayer perceptron(MLP), at least one convolutional neural network (CNN), at least onerecurrent neural network (RNN), at least one autoencoder, at least onetransformer, and/or the like). In some examples, perception system 402,planning system 404, localization system 406, and/or control system 408implement at least one machine learning model alone or in combinationwith one or more of the above-noted systems. In some examples,perception system 402, planning system 404, localization system 406,and/or control system 408 implement at least one machine learning modelas part of a pipeline (e.g., a pipeline for identifying one or moreobjects located in an environment and/or the like).

Database 410 stores data that is transmitted to, received from, and/orupdated by perception system 402, planning system 404, localizationsystem 406 and/or control system 408. In some examples, database 410includes a storage component (e.g., a storage component that is the sameas or similar to storage component 308 of FIG. 3 ) that stores dataand/or software related to the operation and uses at least one system ofautonomous vehicle compute 400. In some embodiments, database 410 storesdata associated with 2D and/or 3D maps of at least one area. In someexamples, database 410 stores data associated with 2D and/or 3D maps ofa portion of a city, multiple portions of multiple cities, multiplecities, a county, a state, a State (e.g., a country), and/or the like).In such an example, a vehicle (e.g., a vehicle that is the same as orsimilar to vehicles 102 and/or vehicle 200) can drive along one or moredrivable regions (e.g., single-lane roads, multi-lane roads, highways,back roads, off road trails, and/or the like) and cause at least oneLiDAR sensor (e.g., a LiDAR sensor that is the same as or similar toLiDAR sensors 202 b) to generate data associated with an imagerepresenting the objects included in a field of view of the at least oneLiDAR sensor.

In some embodiments, database 410 can be implemented across a pluralityof devices. In some examples, database 410 is included in a vehicle(e.g., a vehicle that is the same as or similar to vehicles 102 and/orvehicle 200), an autonomous vehicle system (e.g., an autonomous vehiclesystem that is the same as or similar to remote AV system 114, a fleetmanagement system (e.g., a fleet management system that is the same asor similar to fleet management system 116 of FIG. 1 , a V2I system(e.g., a V2I system that is the same as or similar to V2I system 118 ofFIG. 1 ) and/or the like.

Referring now to FIGS. 5A-5C, illustrated are diagrams of one or moreimplementations 500 of a process for agent prioritization, and/or agentfiltering, e.g. for determining an autonomous vehicle trajectory. Insome embodiments, implementation 500 includes an AV compute 540, and avehicle 502 (similar to vehicle 200 of FIG. 2 , such as an autonomousvehicle). In some embodiments, system 500 is the same as or like system,such as a remote AV system, a fleet management system, a V2I system.

Advantageously, autonomous vehicles, systems, methods, and computerprogram products are disclosed that can perform agent filtration and/orprioritization, such as for motion prediction and planning oftrajectories. For example, agents can be filtered and/or prioritized inthe processing pipeline (such as via the AV compute 540). For example,agents can be filtered and/or prioritized based on their determinedimportance, as well as other features. The importance of a particularagent can be due to a possible interaction with the autonomous vehicle.Low-fidelity techniques can be applied to agents with a lowerimportance. This can allow for important agents to benefit from highfidelity techniques, which can be more computationally expensive. Aswill be detailed below, to identify the importance of an agent, one ormore various techniques can be used, to determine an interactionparameter indicative of a prediction of interaction of a correspondingagent with the autonomous vehicle. Examples of such techniques compriseone or more of: homotopy extraction, Euclidean distance reachability,road infrastructure reachability, and geometric collision. For furthercomputational efficiency, agents can be clustered. The filtering and/orprioritization techniques can be used at any stage of the processingpipeline: agent recognition, agent prediction, agent projection, motionplanning, etc. For example, the filtering and/or prioritizationtechniques can be used for prediction and/or motion planning.

Advantageously, the disclosure can improve the computational efficiencyof the agent processing pipeline involved in providing trajectories forthe autonomous vehicle. For example, the disclosure can lead to reducedcomputational times for the current stack, such as due to having lessagents to process. Filtration can reduce the number of agents to beprocessed. Prioritization can enable algorithms with reduced fidelity,hence faster, to be used on less important agents. New type ofalgorithms available may only be run on the most important agents. Sincethis method allows using higher fidelity and more computationallyexpensive models on just the most important agents, the autonomousvehicle can be made more aggressive and reactive. The overallcomputational load can be reduced as well.

For example, a task of motion planning of an autonomous vehicle can beto find a lawful and/or comfortable trajectory based on one or moreagents within the environment. Even though a large number of agents maybe present, only a smaller subset of the agents may be impactful to atrajectory of the autonomous vehicle. As the processing of agents is acomputationally intensive operation, and the available resources arelimited, it may be advantageous to determine a subset of agents, forexample based on a prioritization of agents and/or a filtering ofagents.

For example, some agents can be discarded, such as filtered out, oncethere is a determination that the agent is not likely to impact thetrajectory of the autonomous vehicle, such as in a meaningful way.Processing these “unimportant” agents can take a significant amount oftime. Alternatively, or in conjunction with filtering, the priority ofthe agent can be considered as well. By prioritizing agents, one or moreof lower resolution models can be used, lower numerical accuracy can beneed, faster predictors and/or networks can be used, and fewer and/orlimited numbers of iterations in iterative algorithms may be used.

FIG. 5A illustrates a portion of a system illustrating a processingpipeline, such as of AV compute 540, which can be used for an autonomousvehicle, specifically including, in some examples, the perception system514 and the planning system 516. The perception system 514 and theplanning system 516 can be similar to those discussed in FIG. 4 .

In one or more example systems, the system can include at least oneprocessor. In one or more example systems, the system can include atleast one memory storing instructions thereon that, when executed by theat least one processor, cause the at least one processor to obtainsensor data. The sensor data can be associated with an environment inwhich the autonomous vehicle is operating. In one or more examplesystems, the system can include at least one memory storing instructionsthereon that, when executed by the at least one processor, cause the atleast one processor to determine an agent set. The at least oneprocessor can determine the agent set based on the sensor data. Theagent set can include a plurality of agents located in the environment.In one or more example systems, the system can include at least onememory storing instructions thereon that, when executed by the at leastone processor, cause the at least one processor to determine aninteraction parameter. The at least one processor can determine aninteraction parameter for each agent of the agent set. The interactionparameter can be indicative of a prediction of an interaction of thecorresponding agent with the autonomous vehicle. For example, when theagent comprises at least one agent, the interaction parameter can beindicative of a prediction of an interaction between the at least oneagent and the autonomous vehicle. In one or more example systems, thesystem can include at least one memory storing instructions thereonthat, when executed by the at least one processor, cause the at leastone processor to determine a primary agent set. The at least oneprocessor can determine the primary agent set based on the interactionparameters. The primary agent set can be a subset of the agent set. Inone or more example systems, the system can include at least one memorystoring instructions thereon that, when executed by the at least oneprocessor, cause the at least one processor to generate a trajectory ofthe autonomous vehicle. The at least one processor can generate atrajectory of the autonomous vehicle based on the primary agent set.

The at least one processor can be a processor of a sensor. The at leastone processor can be a processor of an AV compute 540. The at least oneprocessor can be a processor of an autonomous vehicle.

As shown in FIG. 5A, the perception system 402 can obtain, such asreceive, sensor data 502. The sensor data 502 can be received from oneor more sensors associated with the autonomous vehicle. The autonomousvehicle may include one or more sensors, for example, configured toprovide the sensor data 502. The one or more sensors can be configuredto detect a plurality of agents in an environment of the autonomousvehicle. The sensor data 502 can be, for example, one or more of:infrared data, LIDAR data, motion data, and picture data. The sensordata 502 can be associated with an environment in which an autonomousvehicle is operating. The sensors can be any and/or all of the sensorsdiscussed above. While there is no limit to the dimensions of theenvironment, the environment may be anywhere that the autonomous vehiclecan acquire sensor data in. This can include sensor attached with theautonomous vehicle, or data received from sensors external to thevehicle.

The sensor data 502 may be used for agent recognition 504, such as anagent recognition scheme. For example, the at least one processor candetermine, based on the sensor data 502, an agent set including aplurality of agents located in the environment. The agent set caninclude one or more agents located in the environment.

An agent may be seen as any item or object that can be perceived in theenvironment of the autonomous vehicle. In some examples, an agent may beanything capable of dynamic movement over time. In some examples, anagent may be anything that can be viewed as: perceiving its environmentthrough sensors and/or acting upon that environment through actuators.

Agents can be all physical objects located in the environment. Agentscan be some physical objects located in the environment. The type ofagent can be set by a system or a user. The type of agent can bedetermined automatically, such as through machine learning. Exampleagents include, but are not limited to, other vehicles, pedestrians,bikers, road obstructions, signs, and intersections. In one or moreexample systems, an agent of the plurality of agents can include anobject capable of a dynamic movement over time. An agent can be anyobject that is captured by a sensor, or included in a sensor data. Forexample, a road user and/or road equipment can be an agent.

An agent can be determined via one or more criteria. Example criteriainclude movement, anticipated movement, and acceleration. An agent canbe stored in a database. An agent can be determined via machinelearning. An agent can be determined via a neural network model.

The agent set can include data relevant to the particular agent. Theagent set can include data associated with the particular agent, forexample via an agent identifier identifying the agent. The agent set canbe a database. The agent set can be a table. The agent set can beordered. The agent set can be disordered. The agent set can be a list ofagents, such as a list of agent identifiers. The agent set can include 0agents. The agent set can include at least one agent. In other words,the system can determine, based on the sensor data, an agent setincluding at least one agent. The agent set can include a plurality ofagents.

For one or more agents of the agent set, agent prediction 506, such asan agent prediction scheme, can be performed. For each agent of theagent set, agent prediction 506, such as an agent prediction scheme, canbe performed. The agent prediction 506 can predict, such as based on aprobability, an action of an agent. Example actions are movement, speed,direction, and acceleration. For example, the agent prediction 506 caninclude determining an interaction parameter indicative of a predictionof the corresponding agent with the autonomous vehicle. The interactionparameter may be a probability of interaction. The interaction parametermay be indicative of dimensions of an agent, such as speed and/ordirection.

The interaction parameter may be indicative of interaction of thecorresponding agent for the trajectory of the autonomous vehicle, e.g.,due to potential interaction, potential conflict, potential collision.For example, the interaction parameter may indicate a potential futureinteraction of the corresponding agent with the autonomous vehicle, suchas a predicted interaction, such as a probability of the agentinteracting with the autonomous vehicle. For example, the interactionparameter may indicate prediction of a level of interaction of thecorresponding agent interacting with the autonomous vehicle.

Further, for one or more (such as for each) agent of the agent set,agent projection 508, such as an agent projection scheme, can beperformed. While the agent prediction 506 may predict where an agent islikely to go (such as will go), the agent projection 508 may actuallyproject where the agent is likely to go (such as will go). Theinteraction parameter can be determined via the agent prediction 506and/or the agent projection.

In one or more example systems, to determine, by the at least oneprocessor, for each agent of the agent set, an interaction parameterindicative of a prediction of interaction of the corresponding agentwith the autonomous vehicle can include to predict an interaction of thecorresponding agent with the autonomous vehicle. In one or more examplesystems, to determine, by the at least one processor, for each agent ofthe agent set, an interaction parameter indicative of a prediction ofinteraction of the corresponding agent with the autonomous vehicle caninclude to determine the interaction parameter based on the prediction.

For example, the agent prediction 506 may predict the interactionparameter of the corresponding agent with the autonomous vehicle. Theinteraction parameter may be indicative of an interaction.

An interaction can be a physical interaction between an agent and theautonomous vehicle. An interaction can be with the autonomous vehicleitself. An interaction can be with an area around the autonomousvehicle. For example, the autonomous vehicle may have a defined boundaryaround the autonomous vehicle.

In one or more example systems, to predict the interaction parameter ofthe corresponding agent with the autonomous vehicle, such as during theagent prediction 506 and/or the agent projection 508, can include todetermine, for each agent, one or more homotopy parameters. The one ormore homotopy parameters can be indicative of a constraint applied bythe agent on a trajectory of the autonomous vehicle. In one or moreexample systems, to predict the interaction of the corresponding agentwith the autonomous vehicle can include to predict the interaction basedon the one or more homotopy parameters.

The one or more homotopy parameters can include potential constraints ofan agent. The one or more homotopy parameters can be indicative of aparticular physical characteristic of an agent. In one or more examplesystems, the one or more homotopy parameters are indicative of one ormore of: a speed of the agent, an acceleration of the agent, and alocation of the agent. The one or more homotopy parameters can include anumber of homotopies. The one or more homotopy parameters can include asize of a homotopy. The homotopy parameters are not limited to theabove, and other parameters can be used as well.

In one or more example systems, to predict the interaction based on theone or more homotopy parameters can include predicting the interactionby inputting the one or more homotopy parameters or sensor data into aneural network model, such as discussed with respect to FIG. 5B and FIG.5C. The neural network model may be a machine learning model. The neuralnetwork model may be and/or include an artificial intelligence model.The neural network model may output a prediction. The neural network mayoutput an interaction parameter. Multiple features or parameters can becombined into a scalar number which can be used to train the neuralnetwork. For example, an offline-planner may be used to annotate adataset with the final importance factor. Real-time requirements can bedropped. Ground truth predictions can be utilized. In some embodiments,system 500 can use a neural network model to generate at least oneprediction to provide the interaction parameter. In an example, system500 can use a neural network model to generate at least one predictionbased on system 500 providing homotopy parameter(s) to the neuralnetwork model. In such an example, the neural network model can generateat least one prediction based on the homotopy parameter(s). The at leastone prediction can include a prediction of a probability of the agentinteracting with the autonomous vehicle.

Based on the interaction parameters, the autonomous vehicle compute 540can determine a primary agent set. The primary agent set can be theagent set. The primary agent set can be a subset of the agent set. Theprimary agent set can be a proper subset of the agent set. For example,the primary agent set can be not equal to the agent set. For example,the agent set can include at least one element, such as an agent, whichis not in the primary agent set. The primary agent set can be arearrangement of the agent set. The primary agent set can be aconfirmation of the primary agent set. The primary agent set can includethe same number of agents as the agent set. The primary agent set caninclude less agents than the agent set. The primary agent set can be theresult of a filtering of the agent set. The primary agent set can be theresult of a prioritization scheme applied to the agent set.

In one or more example systems, to determine, based on the interactionparameters, the primary agent set can include to determine the primaryagent set by filtering out, such as removing, eliminating, excluding,from the agent set, one or more agents of the agent set based on theinteraction parameters. For example, filtering can be applied to theagent set to determine the primary agent set. The filtering may reducethe number of agents between the agent set and the primary agent set.The filtering may not reduce the number of agents between the agent setand the primary agent set. The filtering may remove one or more agentsfrom the agent set to determine the primary agent set.

The filtering may be based on the interaction parameter. For example, ifthere is a low probability of an agent interacting with the autonomousvehicle, as represented by the interaction parameter, the agent may befiltered out.

In one or more example systems, the filtering can be based on acriterion applied to the interaction parameters. For example, thecriterion may be based on a threshold. The criterion may be aprobability threshold of the agent interacting with the autonomousvehicle. In accordance with the interaction parameter of a correspondingagent meeting the criterion, the corresponding agent may be filtered outin the determination of the primary agent set. In accordance with theinteraction parameter of a corresponding agent not meeting thecriterion, the corresponding agent may not be filtered out in thedetermination of the primary agent set. Agents which are not filteredout can be included in the primary agent set.

Different filtering schemes can be used for the filtering out. Forexample, one or more of: agent line-of-sight distance and velocity(Euclidian distance reachability radius), road infrastructurereachability (agents in an opposite lane on a road with lane dividerbarriers), and geometric collisions between agent projected polygons andautonomous vehicle possible paths. Euclidean distance-based radii cancache computed distance radii for further filters. Road infrastructurereachability can use cached distance, can expand reachable road networkgraphs, and can cache road segments with agents. Geometric collisionsmay be checked for autonomous vehicle segments corresponding to roadsegments cached with an agent.

In one or more example systems, to determine, based on the interactionparameters, the primary agent set can include to determine the primaryagent set by applying, to the agent set, a prioritization scheme basedon the interaction parameters. The prioritization scheme can be appliedinstead of the filtering. The prioritization scheme can be applied inconjunction with the filtering. The prioritization scheme may be appliedbefore the filtering. The prioritization scheme may be applied duringthe filtering. The prioritization scheme may be applied after thefiltering. Agents that the prioritization scheme is applied to may beincluded in the primary agent set.

The prioritization scheme may be stored in the autonomous vehiclecompute 540. The prioritization scheme may be determined by theautonomous vehicle compute 540. The prioritization scheme may bedetermined in real time.

The prioritization scheme may be used to rearrange agents in the agentset and/or the primary agent set. For example, the prioritization schemecan reorder agents in the agent set and/or the primary agent set. Theprioritization scheme can reorder agents in the agent set and/or theprimary agent set based on likelihood of interaction with the autonomousvehicle. The prioritization scheme can reorder agents in the agent setand/or the primary agent set based on a prediction of interaction withthe autonomous vehicle. The prioritization scheme can reorder agents inthe agent set and/or the primary agent set based on probability ofinteraction with the autonomous vehicle.

The prioritization scheme may provide a prioritization order to theprimary agent set as compared to the agent scheme. For example, theprioritization scheme may order the agents in the agent set based on aprioritization of interaction of the agents with the autonomous vehicle.The prioritization scheme may be used to determine an order of“importance” of the agents in the agent set and/or the primary agentset.

In one or more example systems, the prioritization scheme can be basedon a criterion applied to the interaction parameters. In accordance withthe interaction parameter of a corresponding agent meeting thecriterion, the corresponding agent may be moved to a higher priority inthe determination of the primary agent set. In accordance with theinteraction parameter of a corresponding agent not meeting thecriterion, the corresponding agent may not be moved to a higher priorityin the determination of the primary agent set.

The filtering and/or prioritization scheme can be used, generally, topredict importance of agents. The importance of the agent can be basedon the interaction parameter of the agent. The interaction parameter ofthe agent can be based on, for example, one or more of: an effect ofeach agent on a number and/or size of homotopies, an effect of eachagent on properties of the final trajectory of the autonomous vehicle(such as acceleration), independent effects of each agent, dependenteffects of each agent based on other agents.

While the filtering and/or prioritization scheme is discussed withrespect to the determining the primary agent set, the filtering and/orprioritization scheme can be used at many different points during theoperation of the autonomous vehicle compute 540. New primary agent sets,such as a first primary agent set, a second primary agent set, a thirdprimary agent set, etc. can be determined at different processingpoints.

For example, FIG. 5A shows an agent filtration identification point 501.This agent filtration identification point 501 can be representative ofthe filtering and/or prioritization scheme discussed herein, such as adetermining of a primary agent set, and/or such as providing the primaryagent set to any other elements of 500. As shown in FIG. 5A, the agentfiltration identification point 501 can be performed at one or more of:during the agent recognition 504, after the agent recognition 504, afterthe agent prediction 506, after the agent projection 508, after theperception system 514, during the motion planning 510, and after themotion planning 510. The particular time for using the agent filtrationidentification point 501 is not limiting. The agent filtrationidentification point 501 can be configured to provide the primary agentset to one or more of: the agent recognition 504, the agent prediction506, the agent projection 508, the perception system 514, the motionplanning 510.

The agent filtration identification point 501 can be a point in whichthe filtering and/or prioritization scheme can be updated. Therefore,the filtering and/or prioritization scheme may be dynamic.Alternatively, the filtering and/or prioritization scheme may be static.The filtering and/or prioritization scheme can be applied iteratively.The filtering and/or prioritization scheme can be applied once. Thefiltering and/or prioritization scheme can be applied multiple times.The filtering and/or prioritization scheme can be applied continuously.

Advantageously, filtering and/or the prioritization scheme can be usedat different points, such as at a plurality of points, duringprocessing, which can improve computational efficiency. For example,filtering can greatly improve costly stages of the autonomous vehicle,such as maneuver exploration. Further, the cascading nature of thefiltering and/or the prioritization scheme can be advantageous. An agentthat is filtered out early may not be included later in the processingpipeline, thereby improving computational efficiency.

In one or more example systems, the system can cause the at least oneprocessor to cluster agents of the plurality of agents. For example, thesystem can cause the at least one processor to cluster, based on the oneor more homotopy parameters, agents of the plurality of agents. Thehomotopy parameter may indicate an agent trajectory projection onto atrajectory of the autonomous vehicle, such as a projected distancebetween two agents. For example, the homotopy parameter may include aprojected distance between two agents. For example, the system cancluster agents of the plurality of agents based on an agent trajectoryprojection onto a trajectory of the autonomous vehicle. If the agenttrajectory projection is close enough between two agents, the two agentscan be clustered together. The clustering of agents of the plurality ofagents can be based on a projected distance between agents. The systemcan cluster agents based on the interaction parameter of thecorresponding agents. The system can cluster agents based on one or morehomotopy parameters. For example, the homotopy parameter may include aninteraction parameter of the corresponding agents.

For example, the automotive vehicle compute 540 can cluster togetherrelated agents. This can improve computational efficiency. For example,if the autonomous vehicle is located near a cross walk, a number ofpedestrians may cross the cross walk. Instead of individuallydetermining and/or tracking each of the pedestrians as an agent, whichmay require high computational power, the system can be configured tocluster the pedestrians together as a single agent. This can be basedon, for example, homotopy parameters indicating that the group ofpedestrians are moving at approximately the same speed and the samedirection. For example, the overall impact of a set of agents may bedetermined to be similar.

For example, an impact of agents in a set of agents may be similar toeach other, suggesting that the agents can be clustered together duringtraining and execution. For example, the impact of multiple pedestrianscrossing the road simultaneously would be similar and thefiltration/prioritization scheme applied to one could be applied to all.The clustering can be performed during the determination of agents forthe agent set. The clustering can be performed during the determinationof the primary agent set. As an example, clustering can be a form offiltering agents of the agent set, such as discussed herein.

For example, for each agent, such as based on a homotopy extractor, aclustering parameter for each of the plurality of agents can bedetermined. The clustering parameters for each agent can be compared,such as a first clustering parameter for a first agent and a secondclustering parameter for a second agent. In accordance with the firstclustering parameter and the second clustering parameter both meeting aclustering criterion, the second agent can be filtered out. Inaccordance with either the first clustering parameter or the secondclustering parameter not meeting a clustering criterion, the secondagent may not be filtered out.

The clustering can be performed on lower priority agents. The clusteringcan be performed on higher priority agents. The clustering can beperformed on all agents.

In one or more example systems, the at least one memory storinginstructions thereon that, when executed by the at least one processor,cause the at least one processor to determine, by the at least oneprocessor, a secondary agent set. The second agent set can be determinedbased on the interaction parameters. The primary agent set and thesecondary agent set may be mutually exclusive subsets of the agent set.For example, the primary agent set may not include any agents of thesecondary agent set, and vice versa. The primary agent set and thesecondary agent set may not be mutually exclusive subsets of the agentset. The secondary agent set may include agents that are considered ofless probability of interaction based on their corresponding interactionparameter. For example, an agent may be included in the secondary agentset when the corresponding agent does not meet a criterion. An agent maybe included in the primary agent set when the corresponding agent meetsa criterion.

The primary agent set can be obtained by the planning system 516. Thesecondary agent set can be obtained by the planning system 516. Theplanning system 516 can perform motion planning 510, such as atrajectory extraction scheme. The motion planning 510 can be used fordetermining potential movement, such as motion, of the autonomousvehicle. Based on the motion planning 510, specifically the resultsand/or trajectories output by the motion planning 510, the autonomousvehicle computer 540 can perform trajectory evaluation 512, such as atrajectory evaluation scheme. For example, the motion planning 510 maygenerate one or more potential trajectories of the autonomous vehicle.The trajectory evaluation 512 can then evaluate each of the potentialtrajectories to generate a trajectory for the autonomous vehicle thatcould be performed by the autonomous vehicle. For example, thetrajectory evaluation 512 can obtain the primary agent set forevaluation the potential trajectories. The trajectory evaluation 512 cangenerate, based on the primary agent set, the trajectory for theautonomous vehicle for operating the autonomous vehicle. A trajectory ofan agent may be characterized by one or more trajectory parametersindicative of a position of the agent in space and time. A trajectorymay be seen as a trajectory output including one or more trajectoryparameters.

In one or more example systems, to generate, based on the primary agentset, the trajectory for the autonomous vehicle can include to generate,based on the primary agent set and the secondary agent set, thetrajectory for the autonomous vehicle. The trajectory of the autonomousvehicle can be generated based on only the secondary agent set.

In one or more example systems, to generate, based on the primary agentset, the trajectory for the autonomous vehicle can include applying afirst model to an agent of the primary agent set. For example, the firstmodel can be applied to each agent of the primary agent set.

In one or more example systems, to generate, based on the primary agentset and the secondary agent set, a trajectory for the autonomous vehiclecan include to apply a second model to one or more agents of thesecondary agent set. For example, the second model can be applied toeach agent of the secondary agent set.

The first model may be different from the second model. The first modelmay have different parameters than the second model. In one or moreexample systems, the first model can have a higher fidelity than thesecond model. In one or more example systems, the first model or secondmodel comprises one or more of: an agent recognition scheme, an agentprediction scheme, an agent projection scheme, a trajectory extractionscheme, and a trajectory evaluation scheme. The first model may be, forexample, Game-theoretic interaction aware planning and/or a KraussLane-Follow algorithm. The second model may be, for example, one or moreof: a constant velocity algorithm. These models can be particularlyuseful for the agent prediction 506.

The first model and/or the second model can be used for the agentprediction 506 as well. For example, the prioritization scheme may beuseful for determining the fidelity of geometric representation of theagents. The first model may project using complex, possibly non-convexpolygons. The second model may project using bounding boxes.

The first model and/or the second model can also be used for motionplanning 510. For example, the first model can use constraint extractionwhile the second model may not use constraint extraction. By skippingconstraint extraction for low priority agents, the total computation canbe reduced.

The first model and/or the second model can also be used for trajectoryevaluation 512. For example, the first model can use high sampling ratesfor collision checking while the second model can use low samplingrates.

The agent recognition 504 may be configured to identify the agent basedon the sensor data 502. For example, applying the first model caninclude applying a first agent recognition scheme to the primary agentset. For example, applying the second model can include applying asecond agent recognition scheme to the secondary agent set. For examplethe second agent recognition scheme can have lower fidelity than thefirst agent recognition scheme.

Applying the first model to the agent of the primary agent set caninclude predicting a trajectory of the agent of the primary agent set.For example, applying the second model to the agent of the secondaryagent set can include predicting a trajectory of the agent of thesecondary agent set.

For example, applying the first model to the agent of the primary agentset and/or the second model to the agent of the secondary agent set caninclude projecting a trajectory of the agent onto areas, such asenvironments, associated with the autonomous vehicle. Areas associatedwith the autonomous vehicle may include road surfaces that could be (orwill be) traversed by the autonomous vehicle. When applying the agentprojection 508, the first model for prioritization may be used todetermine the fidelity of the geometric representation of the agent. Forexample, high priority objects of the primary agent set can be projectedusing complex, possibly non-convex polygons, whereas low-priority agentsof the secondary agent set can be projected with simpler bounding boxes.

In certain systems, the second model may be applied to both the primaryagent set and the secondary agent set. The first model can thenadditionally be applied to the primary agent set.

A third model may be applied to a tertiary agent set. A fourth model maybe applied to a quaternary agent set. The number of models and agentsets is not limiting.

In one or more example systems, the system, such as the control system408 and/or the AV compute 540, can cause the at least one processor tooperate, based on the trajectory, the autonomous vehicle. The autonomousvehicle can operate, such as drive, the autonomous vehicle. A processorcan be configured to operate, such as drive, the autonomous vehicle. Thesystem can be configured to drive the autonomous vehicle. The system canbe configured to operate the autonomous vehicle. For example, aplurality of trajectories may be generated and seen as a candidatetrajectories. The autonomous vehicle compute 540 may be configured toselect a trajectory amongst the candidate trajectories and optionallynavigate according to the selected trajectory.

FIG. 5B illustrates a diagram of one or more implementations of aprocess for agent prioritization and/or filtering, e.g. for determiningan autonomous vehicle trajectory. Specifically FIG. 5B illustratesfurther details of the perception system 514 and the planning system516.

As shown, the perception system 514 may receive the sensor data 502. Theperception system 514 may further include a neural network model 520,such as a neural network. The neural network model 520 may encompass oneor more of the agent recognition 504, agent prediction 506, and agentprojection 508 discussed above with respect to FIG. 5A.

The neural network model 520 can include a number of heads, such asnodes, models, parameters, schemes. For example, the neural networkmodel 520 can include one or more of a perception head 522, such as adetection head, a prioritization head 524, such as an importanceclassification head, and a prediction head 526. The perception head 522may, in certain examples, encompass the agent recognition 504 discussedabove. The prediction head 526 may, in certain examples, encompass theagent prediction 506 and agent projection 508 discussed above. Theprioritization head 524, may be used for the determination of theprimary agent set, such as using the filtering and/or prioritizationscheme discussed above.

As shown in FIG. 5B, the neural network model 520 can receive the sensordata 502. The perception head 522 may use the sensor data to determinethe agent set. The agent set can include one or more agents located inthe environment. The agent set can include a plurality of agents locatedin the environment.

The perception head 522 may output the agent set to one or moreadditional heads in the neural network 520.

For example, the perception head may output the agent set to aprediction head 526. The prediction head 526 can utilize the agent set,along with sensor data 502, to predict movement of any agents in theagent set. The prediction head 526 can utilize, for example, machinelearning and/or artificial intelligence to predict the movement of anyagents in the agent set. Moreover, the prediction head 526 maydetermine, for each agent of the agent set, an interaction parameter.The interaction parameter can be indicative of a prediction ofinteraction of a corresponding agent with the autonomous vehicle. Forexample, the prediction head 526 can predict whether or not it is likelythat a particular agent will interact with the autonomous vehicle. Theprediction head 526 can output data indicative of a prediction, as wellas the interaction parameter. For example, the interaction parameter maybe stored along with a particular agent in the agent set.

The perception head 522 may output the agent set to a prioritizationhead 524. The prioritization head 524 may be a rules-basedprioritization head. The prioritization head 524 may be a machinelearning, such as a neural network, based prioritization head. Theprioritization head 524 can provide a prioritization scheme of differentagents, such as in the agent set. The prioritization head 524 may applyfiltering. The prioritization head 524 can determine, such as by atleast one processor, a primary agent set from the agent set. Theprioritization head 524 may determine the primary agent set based on theinteraction parameters from the prediction head 526. Further, theprioritization head 524 can provide an output to the prediction head 526for prioritization of agent analysis.

For example, the prioritization head 524 can receive an output from theprediction head 526, such as the interaction parameter, in order toperform a prioritization. The prioritization head 524 may outputprioritization, such as based on criterion. The prioritization head 524may output prioritization to the prediction head 528.

The perception system 514 may then optionally apply a feature basedprioritization 528 to the primary agent set and/or the agent set. Thefeatures may be homotopies. The features may be interaction parameters.The feature based prioritization may include the filtering and/orprioritization scheme discussed herein. For example, the prioritizationhead 524 may provide the feature bases prioritization 528. Thefeature-based prioritization 528 may rearrange the order of agents inthe primary agent set, such as based on a prioritization scheme. Forexample, the feature-based prioritization may rearrange the agents ofthe primary agent set based on a feature. The feature can be the one ormore homotopy parameters. The feature-based prioritization 528 may alsoprovide filtering of the agent set and/or the primary agent set.

The primary agent set may be output from the perception system 514. Forexample, the primary agent set may be output to the planning system 516.

The planning system 516 may receive an output from the perception system514. For example, the planning system 516 may receive the primary agentset as an input.

The planning system 516 may apply more homotopy searches 530, such ashomotopy extractions to the agents in the primary agent set. Theplanning system 516 may apply more homotopy searches 530, such ashomotopy extractions to the agents in the agent set. The homotopy search530 may be, for example, the motion planning 510 discussed above in FIG.5A.

As shown in FIG. 5B, the homotopy search 530 may produce a potentialtrajectory 532. The homotopy search 530 may produce a number ofpotential trajectories 532, 532A, 532B, and/or homotopies, also known astrajectory realization. There is no particular limit to the number oftrajectories, and any number of trajectory generators can be produced.

Based on the potential trajectories 530, 530A, 530B, the planning system516 can make a trajectory selection 534. This may include the trajectoryevaluation 512 discussed above with respect to FIG. 5A. Further, theplanning system 516 may generate a trajectory of the autonomous vehicle.For example, the planning system 516 may generate , based on the primaryagent set and/or the agent set, a trajectory for the autonomous vehicle.

A trajectory of an agent may be characterized by one or more trajectoryparameters indicative of a position of the agent in space and time. Atrajectory may be seen as a trajectory output including one or moretrajectory parameters.

FIG. 5C illustrates further details of the perception system 514, suchas including the neural network 520. In particular, FIG. 5C illustrateshow one or more models, such as the first model and/or the second model,can be applied to agent sets.

As discussed, the perception system 514 can obtain sensor data 502. Theneural network 520 can further receive the sensor data 502. From thesensor data 502, a number of neural network heads can be used such asthe perception head 522 and the prioritization head 524 discussed above.

The perception head 522 and the prioritization head 524 can worktogether to determine the primary agent set and the secondary agent set.For example, high priority agents can be included in the primary agentset while lower priority agents can be included in the secondary agentset. The prioritization head 524 may be pretrained. The prioritizationhead 524 may be trained.

A first model 542, such as a fine-grained prediction head, may beapplied to the primary agent set. The first model 542 may have a highfidelity, as the primary agent set can include agents that are predictedto interact with the autonomous vehicle. The first model 542 may beexpensive to compute. Accordingly, computational power may be used toprepare more robust analysis of the primary agent set. The first model542 may be pretrained. The first model 542 may be trained.

A second model 544, such as a coarse-grained prediction head, may beapplied to the secondary agent set. The second model 544 may have a lowfidelity, as the secondary agent set can include agents that arepredicted to not interact with the autonomous vehicle. The second model544 may be cheaper to compute. The second model 544 may have a lowerfidelity than the first model 542. Accordingly, computational power maybe reduced, at least as compared to the first model 542, to prepare aless robust analysis of the secondary agent set. The second model 544may be pretrained. The second model 544 may be trained.

Further, the agent filtration identification point 501 can be usedwithin the neural network 520. As shown in FIG. 5C, the agent filtrationidentification point 501 can be used with the prioritization head 524.The agent filtration identification point 501 can be used with the firstmodel 542. Further, the agent filtration identification point 501 couldbe used with the perception head 522 and/or the second model 544.

FIG. 6 illustrates a system for training the autonomous vehicle compute400, such as for a neural network 540. The autonomous vehicle compute400 may operate according to operations of the autonomous vehiclecomputer 540 of FIGS. 5A-5C. This can be done for creating more accurateand/or more efficient trajectory maps. Database 450 stores data that istransmitted to, received from, and/or updated by perception system 402,planning system 404, localization system 406 and/or control system 408.In some examples, database 450 includes a storage component (e.g., astorage component that is the same as or similar to storage component308 of FIG. 3 ) that stores data and/or software related to theoperation and uses at least one system of autonomous vehicle computer400. In some embodiments, database 450 stores data associated with 2Dand/or 3D maps of at least one area. In some examples, database 450stores data associated with 2D and/or 3D maps of a portion of a city,multiple portions of multiple cities, multiple cities, a county, astate, a State (e.g., a country), and/or the like). In such an example,a vehicle (e.g., a vehicle that is the same as or similar to vehicles102 and/or vehicle 200) can drive along one or more drivable regions(e.g., single-lane roads, multi-lane roads, highways, back roads, offroad trails, and/or the like) and cause at least one LiDAR sensor (e.g.,a LiDAR sensor that is the same as or similar to LiDAR sensors 202 b) togenerate data associated with an image representing the objects includedin a field of view of the at least one LiDAR sensor.

In some embodiments, database 450 can be implemented across a pluralityof devices. In some examples, database 450 is included in a vehicle(e.g., a vehicle that is the same as or similar to vehicles 102 and/orvehicle 200), an autonomous vehicle system (e.g., an autonomous vehiclesystem that is the same as or similar to remote AV system 114, a fleetmanagement system (e.g., a fleet management system that is the same asor similar to fleet management system 116 of FIG. 1 , and/or a V2Isystem (e.g., a V2I system that is the same as or similar to V2I system118 of FIG. 1 ) and/or the like. The database 450 can be physicaldatabases, such as included in a vehicle. The database 450 can be avirtual database, such as a cloud database, that can be accessible bythe vehicle.

The database 450, such as for example a database generated from anexpert driver, can be used to provide data input to a neural network540, such as an augmented prediction neural network. The database 450can also provide data to a planner system 454, such as for ground truth.One or both of the neural network 540 and the planner system 454 candetermine a loss indicative of a comparison on how the neural network540 is performing as compared to how the neural network 540 should beperforming. For example, an importance factor can be compared betweenthe neural network 540 and the planner 454. The loss 456 can be outputto one or more of the planner 454, neural network 540, and database 450in order to improve the neural network 540. The loss 456 can be computedbetween the planner 454 and the neural network 540. For example, theloss 456 may be used to update, such as train, one or more of: theperception system 402, the planning system 404, the first model 542, thesecond model 544, the perception head 522, the prioritization head 524,the prediction head 526, the feature based prioritization 528, thehomotopy search 530, the agent recognition 504, the agent prediction506, the agent projection 508, and the motion planning 510. Features ofthe agents can be designated a scalar number, which can be used fortraining.

The planner system 454 may be used to extract homotopy and/or importancecriterion. The planner system 454 can be used to train the neuralnetwork 540. For example, the planner system 454 can be used to extracthow agents affect corridors of travel for the autonomous vehicle. Theneural network 540 can be trained to extract the same information as ifthe planner system 454 was applied to each agent. While the plannersystem 454 may include man made, or preset, rules, the neural network540 can avoid this and instead use machine learning.

For example, the planner system 454 can be run a large number of timesto determine importance of agents on a given scenario. However, this canbe time consuming and expensive to do for every potential interaction ofthe autonomous vehicle. However, the planner system 454 can be used totrain the neural network 540 using the large number of scenarios run.The planner system 454 can be run offline, and thus may not be activeduring the operation of the autonomous vehicle.

Referring now to FIGS. 7A-7B, illustrated are diagrams of animplementation 700 of a process for agent prioritization and/or agentfiltering, e.g. for determining an autonomous vehicle trajectory. Insome embodiments, implementation 700 includes the perception system 402or perception system 514 and the planning system 404 or planning system516 as disclosed in detail above. Further systems, such as thelocalization system 406, control system 408, etc., can be utilized aswell.

Specifically, FIG. 7A illustrates a real-world example implementation700 of a process for agent prioritization, and/or agent filtering, e.g.for determining an autonomous vehicle trajectory.

The autonomous vehicle 702 can include any and/or all of the systems,methods, and medium discussed in detail above. Autonomous vehicle 702may have a direction as indicated in FIG. 7B. The autonomous vehicle 702may obtain sensor data of the environment in which the autonomousvehicle 702 is operating in. The autonomous vehicle 702 can thendetermine, based on the sensor data, an agent set including a pluralityof agents located in the environment.

In FIG. 7A, there are a number of agents that can be determined andincluded in the agent set. For example, there is a first agent 704, asecond agent 706, a third agent 708, a fourth agent 710. The agents 704,706, 708, 710 may be called vehicle agents. The agents can all have acurrent direction as indicated in FIG. 7A.

The autonomous vehicle 702 is configured to determine for each of theagents 704, 706, 708, and 710, an interaction parameter indicative of aprediction of interaction of the corresponding agent with the autonomousvehicle. As first agent 702 is separated from autonomous vehicle 702 bya guard rail, the first agent 702 will have a very low interactionparameter. Similarly, as fourth agent 710 is separated by a lane oftraffic including second agent 706, and is also ahead of the autonomousvehicle 702, the fourth agent 710 will also have a low interactionparameter. As there may be some potential prediction of an interactionfrom the fourth agent 710, it can have a higher interaction parameterthan the first agent 704.

Moving next to the second agent 706, it is both close to and in linewith the autonomous vehicle 702. Accordingly, as there is a highprediction of interaction, the second agent 706 may have a highinteraction parameter.

The third agent 708 is separated by a lane of traffic, but is locatedbehind the autonomous vehicle. It may be predicted to have a relativelylow interaction, and thus a low interaction parameter. Depending on thesituational awareness, it may have a higher, lower, or equivalentinteraction parameter than the fourth agent 710. For convenience, itwill be assumed that the third agent 708 has an interaction parameterbetween that of the second agent 706 and the fourth agent 710.

Accordingly, the autonomous vehicle 702 can determine, based on theinteraction parameters of the agents 704, 706, 708, and 710, a primaryagent set. As discussed, the autonomous vehicle 702 may filter outand/or prioritize the plurality of agents to determine the primary agentset.

In the situation shown in FIG. 7A, the first agent 704 may have aninteraction parameter that meets a criterion. Accordingly, the firstagent 704 may be filtered out, and the autonomous vehicle 702 may not beconcerned with the first agent 704.

A prioritization scheme may be applied to the remaining agents 706, 708,and 710, for example based on the criterion. Accordingly, the agents maybe prioritized, from high priority to low priority, second agent 706,third agent 708, and fourth agent 710 in the determination of theprimary agent set. The autonomous vehicle 702 may then generate, basedon the primary agent set, the trajectory of the autonomous vehicle 702.Further, the autonomous vehicle 702 may operate along the trajectory.

Alternatively, the second agent 706 may be determined to be included inthe primary agent set, while the third agent 708 and the fourth agent710. A first model may be applied to the primary agent set having thesecond agent 706, which may have a high fidelity. This can beadvantageous as there is a high prediction that the second agent 706 mayinteract with the autonomous vehicle 702. On the other hand, a secondmodel may be applied the secondary agent set having the third agent 708and the fourth agent 710, which may have a low fidelity. While theautonomous vehicle 702 is still concerned with the third agent 708 andthe fourth agent 710, unlike the first agent 704, the prediction ofinteraction is low and thus a lower fidelity model can be used. This canadvantageously save computational power of the autonomous vehicle 702.

Further agents that can be determined and included in the agent set arethe agents 720A, 720B, 720C, 720D. For example, agents 720A, 720B, 720C,720D can be called pedestrian agents. It can be computationallyinefficient to track all of the agents 720A, 720B, 720C, 720D in orderto include them in the agent set. Accordingly, the autonomous vehicle702 can be configured to cluster the agents 720A, 720B, 720C, 720Dtogether, if the autonomous vehicle 702 determines that they are likelymoving in approximately the same speed and direction, for example.Accordingly, the autonomous vehicle 702 can treat the agents 720A, 720B,720C, 720D as a single combined agent 722. Therefore, the autonomousvehicle 702 can determine the interaction parameter for the combinedagent 722, instead of determining an interaction parameter for each ofthe agents 720A, 720B, 720C, 720D. This can save computational power.

FIG. 7B illustrates an example implementation 700 of a process for agentprioritization, and/or agent filtering, e.g. for determining anautonomous vehicle trajectory. As shown, an autonomous vehicle 702 mayuse sensors to determine sensor data. Based on the sensor data, theautonomous vehicle 702 may determine an agent set and a primary agentset 714 as discussed in detail above. The primary agent set can beobtained by the planning system 404, as detailed above. The planningsystem 404 can transmit, or output, a trajectory 716. For example, theplanning system 404 can transmit a trajectory 716 to a control system408. The control system 408 can be used to operate the autonomousvehicle 702, e.g., via a command indicative of the trajectory 716.

Referring now to FIG. 8 , illustrated is a flowchart of a process 800for agent prioritization, and/or agent filtering, e.g. for determiningan autonomous vehicle trajectory. The method can be performed by asystem disclosed herein, such as an AV compute 400, and a vehicle 102,200, 300, 702 of FIGS. 1, 2, 3, 4, 5A-5C, 6, and 7A-B. The systemdisclosed can include at least one processor which can be configured tocarry out one or more of the operations of method 800.

The method 800 can be a method for operating an autonomous vehicle. Inone or more example methods, the method 800 can include obtaining, atstep 802, by at least one processor, sensor data associated with anenvironment in which an autonomous vehicle is operating. In one or moreexample methods, the method 800 can include determining 804, by the atleast one processor, based on the sensor data, an agent set including aplurality of agents located in the environment. In one or more examplemethods, the method 800 can include determining 806, by the at least oneprocessor, for each agent of the agent set, an interaction parameterindicative of a prediction of interaction of a corresponding agent withthe autonomous vehicle. In one or more example methods, the method 800can include determining 808, by the at least one processor, based on theinteraction parameters, a primary agent set, wherein the primary agentset is a subset of the agent set. In one or more example methods, themethod 800 can include generating 810, by the at least one processor,based on the primary agent set, a trajectory for the autonomous vehicle.

The autonomous vehicle can include at least one sensor. The sensor datacan be from the at least one sensor. The sensor data can be from one ormore sensors associated with the autonomous vehicle.

The interaction parameter can be indicative of an interaction of thecorresponding agent for the trajectory of the autonomous vehicle, e.g.,due to potential interaction, a potential conflict, and/or potentialcollision. For example, the interaction parameter may be indicative of apotential future interaction of the corresponding agent with theautonomous vehicle, such as a predicted interaction, such as aprobability of the agent interacting with the autonomous vehicle. Forexample, the interaction parameter may be indicative of a prediction ofa level of interaction of the corresponding agent interacting with theautonomous vehicle.

The primary agent set may be a proper subset of the agent set.

A trajectory may be characterized by one or more trajectory parametersindicative of a position of the agent in space and time. A trajectorymay be seen as a trajectory output including one or more trajectoryparameters.

In one or more example methods, determining, based on the interactionparameters, the primary agent set can include determining the primaryagent set by filtering out one or more agents of the agent set based onthe interaction parameters.

The agents of the agents set that are not filtered out may be includedin the primary agent set.

In one or more example methods, determining, based on the interactionparameters, the primary agent set can include determining the primaryagent set by applying, to the agent set, a prioritization scheme basedon the interaction parameters.

The agents of the agent set that are prioritized can be included in theprimary agent set.

In one or more example methods, the filtering or the prioritizationscheme is based on a criterion applied to the interaction parameters.

In one or more example methods, the filtering and/or the prioritizationscheme is based on a criterion applied to the interaction parameters.

In other words, the method can include determining whether aninteraction parameter of a corresponding agent meets a criterion, suchas a threshold. For example, an agent may be included in the primaryagent set when the interaction parameter of the agent meets thecriterion.

In one or more example methods, generating, based on the primary agentset, the trajectory for the autonomous vehicle includes applying a firstmodel to an agent of the primary agent set.

In one or more example methods, the method 800 can further includedetermining, by the at least one processor, based on the interactionparameters, a secondary agent set, wherein the primary agent set and thesecondary agent set are mutually exclusive subsets of the agent set.

For example the secondary agent set can include agents that areconsidered of less interaction, such as based on their interactionparameter. For example, an agent can be included in the secondary agentset when the interaction parameter of the agent does not meet thecriterion.

In one or more example methods, generating, based on the primary agentset, the trajectory for the autonomous vehicle can include generating,based on the primary agent set and the secondary agent set, thetrajectory for the autonomous vehicle.

In one or more example methods, generating, based on the primary agentset and the secondary agent set, a trajectory for the autonomous vehiclecan include applying a second model to one or more agents of thesecondary agent set.

A third model may be applied, a fourth model may be applied, a fifthmodel may be applied, etc. The second model may be different than thefirst model.

In one or more example methods, the first model can have a higherfidelity than the second model.

In one or more example methods, the first model or second model caninclude one or more of: an agent recognition scheme, an agent predictionscheme, an agent projection scheme, a trajectory extraction scheme, anda trajectory evaluation scheme.

The method, such as the agent recognition scheme, may be configured toidentify an agent based on the sensor data. For example, applying thefirst model can include applying a first agent recognition scheme to theprimary agent set. For example, applying the second model can includeapplying a second agent recognition scheme to the secondary agent set.For example the second agent recognition scheme can have a lowerfidelity than the first agent recognition scheme. For example, applyingthe first model to an agent of the primary agent set can includepredicting a trajectory of the agent of the primary agent set. Forexample, applying the second model to an agent of the secondary agentset can include predicting a trajectory of the agent of the secondaryagent set.

For example, applying the first model to an agent of the primary agentset and/or the second model to an agent of the secondary agent set caninclude projecting a trajectory of the agent onto areas associated withthe autonomous vehicle, such as an environment. Areas associated withthe autonomous vehicle may include road surfaces that could be (or willbe) traversed by the autonomous vehicle. When projecting a detectedagent onto the autonomous vehicle's path, the first model forprioritization may be used to determine the fidelity of the geometricrepresentation of the agent, e.g., high priority objects of the primaryagent set can be projected using complex, possibly non-convex polygons,whereas low-priority agents of the secondary agent set can be projectedwith simpler bounding boxes.

For trajectory extraction, constraints can be applied on the autonomousvehicle's intended trajectory from each agent detected. By prioritizingagents and objects that are more constraining, constraint extraction canbe skipped for less-constraining agents and thus reduce the totalcomputation.

Regarding trajectory evaluation, when evaluating a trajectory, the AVcompute can check for collisions with detected objects. Low-priorityobjects could run collision checking with a lower sampling rate thanhigher priority objects.

In one or more example methods, determining, by the at least oneprocessor, for each agent of the agent set, an interaction parameterindicative of a prediction of interaction of the corresponding agentwith the autonomous vehicle can include predicting an interaction of thecorresponding agent with the autonomous vehicle, and determining theinteraction parameter based on the prediction. In one or more examplemethods, determining, by the at least one processor, for each agent ofthe agent set, an interaction parameter indicative of a prediction ofinteraction of the corresponding agent with the autonomous vehicle caninclude predicting an interaction of the corresponding agent with theautonomous vehicle. In one or more example methods, determining, by theat least one processor, for each agent of the agent set, an interactionparameter indicative of a prediction of interaction of the correspondingagent with the autonomous vehicle can include determining theinteraction parameter based on the prediction.

In one or more example methods, predicting the interaction of thecorresponding agent with the autonomous vehicle can include determining,for each agent, one or more homotopy parameters indicative of aconstraint applied by the agent on a trajectory of the autonomousvehicle, and predicting the interaction based on the one or morehomotopy parameters. In one or more example methods, predicting theinteraction of the corresponding agent with the autonomous vehicle caninclude determining, for each agent, one or more homotopy parametersindicative of a constraint applied by the agent on a trajectory of theautonomous vehicle. In one or more example methods, predicting theinteraction of the corresponding agent with the autonomous vehicle caninclude predicting the interaction based on the one or more homotopyparameters.

The homotopy parameter may include a number of homotopies. The homotopyparameter may include a size of an homotopy.

In one or more example methods, predicting the interaction based on theone or more homotopy parameters can include predicting the interactionby inputting the one or more homotopy parameters or sensor data into aneural network model.

In one or more example methods, predicting the interaction based on theone or more homotopy parameters can include predicting the interactionby inputting the one or more homotopy parameters and/or sensor data intoa neural network model.

Multiple features or parameters can be combined into a scalar number,which can be used to train a neural network model. An offline-plannermay be used to annotate a dataset with the final importance, such as ata factory. Real-time requirement(s) can be dropped. Ground truthpredictions can be utilized. Further, curation of the data can be usedfor training of the neural network model.

In one or more example methods, the one or more homotopy parameters canbe indicative of one or more of: a speed of the agent, an accelerationof the agent, and a location of the agent.

In one or more example methods, the method 800 can include clustering,based on the one or more homotopy parameters, agents of the plurality ofagents.

The impact of agents may be similar to each other, suggesting that theagents can be clustered together during training and execution. Forexample, the impact of multiple pedestrians crossing the roadsimultaneously could be similar, and the filtration/prioritizationscheme applied to one of the pedestrians could be applied to all. Forexample, the method can include determining, based on the homotopyextractor, a clustering parameter for each of the plurality of agents.The method can include comparing the clustering parameter of a firstagent of the plurality of agents with a clustering parameter of a secondagent of the plurality of agents. In accordance with the firstclustering parameter and the second clustering parameter both meeting aclustering criterion, the method can include filtering out the secondagent.

In one or more example methods, an agent of the plurality of agents caninclude an object capable of a dynamic movement over time.

An agent can be any object that is captured by sensors, such as a roaduser, and/or road equipment.

In one or more example methods, the method 800 can include: operating,based on the trajectory, the autonomous vehicle.

For example, a plurality of trajectories may be generated and seen as acandidate trajectories. The autonomous vehicle compute may be configuredto select a trajectory amongst the candidate trajectories and optionallynavigate according to the selected trajectory.

Further disclosed herein is a method for operating an autonomousvehicle. In one or more example methods, the method can includeobtaining, by at least one processor, sensor data associated with anenvironment in which an autonomous vehicle is operating. In one or moreexample methods, the method can include determining, by the at least oneprocessor, based on the sensor data, an agent set comprising a pluralityof agents located in the environment. In one or more example methods,the method can include determining, by the at least one processor, aprimary agent set by applying, to the agent set, a prioritization schemebased on the sensor data. In one or more example methods, the method caninclude generating, by the at least one processor, based on the primaryagent set, a trajectory for the autonomous vehicle.

FIG. 9 illustrates one of the advantages of the disclosed embodiments,systems, methods, and computer program products. The y-axis illustratesthe latency of trajectory extraction timeline using different aspects ofthe disclosure along the x-axis, which can for example illustrate thecomputational time. The y-axis may be understood to representcomputational time, generally. Different types of models are shownincluding a high fidelity model 902, a medium fidelity model 904, and alow fidelity model 906 for prediction head 526, for homotopy search 530,trajectory realization 532, trajectory selection 534 (e.g. illustratedin connection with FIG. 5B). The first model may be the high fidelitymodel 902, and the second model may be either the medium fidelity model904 or the low fidelity model 906. Alternatively, the first model may bethe medium fidelity model 904 and the second model may be the lowfidelity model 906.

As shown, there may be strong computational efficiency advantages tousing the disclosed embodiments, systems, methods, and computer programproducts for agent prioritization, and/or agent filtering, e.g. fordetermining an autonomous vehicle trajectory.

In the foregoing description, aspects and embodiments of the presentdisclosure have been described with reference to numerous specificdetails that can vary from implementation to implementation.Accordingly, the description and drawings are to be regarded in anillustrative rather than a restrictive sense. The sole and exclusiveindicator of the scope of the invention, and what is intended by theapplicants to be the scope of the invention, is the literal andequivalent scope of the set of claims that issue from this application,in the specific form in which such claims issue, including anysubsequent correction. Any definitions expressly set forth herein forterms contained in such claims shall govern the meaning of such terms asused in the claims. In addition, when we use the term “furthercomprising,” in the foregoing description or following claims, whatfollows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

Also disclosed are methods, non-transitory computer readable media, andsystems according to any of the following items:

Item 1. A method for operating an autonomous vehicle, the methodcomprising:

-   -   obtaining, by at least one processor, sensor data associated        with an environment in which an autonomous vehicle is operating,    -   determining, by the at least one processor, based on the sensor        data, an agent set comprising a plurality of agents located in        the environment;    -   determining, by the at least one processor, for each agent of        the agent set, an interaction parameter indicative of a        prediction of interaction of a corresponding agent with the        autonomous vehicle;    -   determining, by the at least one processor, based on the        interaction parameters, a primary agent set, wherein the primary        agent set is a subset of the agent set; and    -   generating, by the at least one processor, based on the primary        agent set, a trajectory for the autonomous vehicle.        Item 2. The method of Item 1, wherein determining, based on the        interaction parameters, the primary agent set comprises        determining the primary agent set by filtering out one or more        agents of the agent set based on the interaction parameters.        Item 3. The method of the previous Items, wherein determining,        based on the interaction parameters, the primary agent set        comprises:    -   determining the primary agent set by applying, to the agent set,        a prioritization scheme based on the interaction parameters.        Item 4. The method of any of Items 2-3 wherein the filtering or        the prioritization scheme is based on a criterion applied to the        interaction parameters.        Item 5. The method of the previous Items, wherein generating,        based on the primary agent set, the trajectory for the        autonomous vehicle comprises applying a first model to an agent        of the primary agent set.        Item 6. The method of any one of the previous Items, further        comprising determining, by the at least one processor, based on        the interaction parameters, a secondary agent set, wherein the        primary agent set and the secondary agent set are mutually        exclusive subsets of the agent set.        Item 7. The method of Item 6, wherein generating, based on the        primary agent set, the trajectory for the autonomous vehicle        comprises:    -   generating, based on the primary agent set and the secondary        agent set, the trajectory for the autonomous vehicle.        Item 8. The method of any of Items 5-7, wherein generating,        based on the primary agent set and the secondary agent set, a        trajectory for the autonomous vehicle comprises:    -   applying a second model to one or more agents of the secondary        agent set.        Item 9. The method of Item 8, wherein the first model has a        higher fidelity than the second model.        Item 10. The method of any one of Items 8-9, wherein the first        model or second model comprises one or more of: an agent        recognition scheme, an agent prediction scheme, an agent        projection scheme, a trajectory extraction scheme and a        trajectory evaluation scheme.        Item 11. The method of any one of the previous Items, wherein        determining, by the at least one processor, for each agent of        the agent set, an interaction parameter indicative of a        prediction of interaction of the corresponding agent with the        autonomous vehicle comprises:    -   predicting an interaction of the corresponding agent with the        autonomous vehicle; and    -   determining the interaction parameter based on the prediction.        Item 12. The method of Item 11, wherein predicting the        interaction of the corresponding agent with the autonomous        vehicle comprises:    -   determining, for each agent, one or more homotopy parameters        indicative of a constraint applied by the agent on a trajectory        of the autonomous vehicle; and    -   predicting the interaction based on the one or more homotopy        parameters.        Item 13. The method of Item 12, wherein predicting the        interaction based on the one or more homotopy parameters        comprises predicting the interaction by inputting the one or        more homotopy parameters or sensor data into a neural network        model.        Item 14. The method of Items 12-13, wherein the one or more        homotopy parameters are indicative of one or more of: a speed of        the agent, an acceleration of the agent, and a location of the        agent.        Item 15. The method of any one of Items 12-13, wherein the        method comprises clustering, based on the one or more homotopy        parameters, agents of the plurality of agents.        Item 16. The method of any one of the previous Items, wherein an        agent of the plurality of agents comprises an object capable of        a dynamic movement over time.        Item 17. The method of any one of the previous Items, wherein        the method comprises: operating, based on the trajectory, the        autonomous vehicle.        Item 18. A non-transitory computer readable medium comprising        instructions stored thereon that, when executed by at least one        processor, cause the at least one processor to carry out        operations comprising:    -   obtaining, by at least one processor, sensor data associated        with an environment in which an autonomous vehicle is operating;    -   determining, by the at least one processor, based on the sensor        data, an agent set comprising a plurality of agents located in        the environment;    -   determining, by the at least one processor, for each agent of        the agent set, an interaction parameter indicative of a        prediction of interaction of the corresponding agent with the        autonomous vehicle;    -   determining, by the at least one processor, based on the        interaction parameters, a primary agent set, wherein the primary        agent set is a subset of the agent set; and    -   generating, by the at least one processor, based on the primary        agent set, a trajectory for the autonomous vehicle.        Item 19. The non-transitory computer readable medium of Item 18,        wherein determining, based on the interaction parameters, the        primary agent set comprises determining the primary agent set by        filtering out one or more agents of the agent set based on the        interaction parameters.        Item 20. The non-transitory computer readable medium of Items        18-19, wherein determining, based on the interaction parameters,        the primary agent set comprises:    -   determining the primary agent set by applying, to the agent set,        a prioritization scheme based on the interaction parameters.        Item 21. The non-transitory computer readable medium of any of        Items 19-20, wherein the filtering or the prioritization scheme        is based on a criterion applied to the interaction parameters.        Item 22. The non-transitory computer readable medium of Items        18-21, wherein generating, based on the primary agent set, the        trajectory for the autonomous vehicle comprises applying a first        model to an agent of the primary agent set.        Item 23. The non-transitory computer readable medium of Items        18-22, further comprising determining, by the at least one        processor, based on the interaction parameters, a secondary        agent set, wherein the primary agent set and the secondary agent        set are mutually exclusive subsets of the agent set.        Item 24. The non-transitory computer readable medium of Item 23,        wherein generating, based on the primary agent set, the        trajectory for the autonomous vehicle comprises:    -   generating, based on the primary agent set and the secondary        agent set, the trajectory for the autonomous vehicle.        Item 25. The non-transitory computer readable medium of any of        Items 22-24, wherein generating, based on the primary agent set        and the secondary agent set, a trajectory for the autonomous        vehicle comprises:    -   applying a second model to one or more agents of the secondary        agent set.        Item 26. The non-transitory computer readable medium of Item 25,        wherein the first model has a higher fidelity than the second        model.        Item 27. The non-transitory computer readable medium of any one        of Items 25-26, wherein the first model or second model        comprises one or more of: an agent recognition scheme, an agent        prediction scheme, an agent projection scheme, a trajectory        extraction scheme and a trajectory evaluation scheme.        Item 28. The non-transitory computer readable medium of Items        18-27, wherein determining, by the at least one processor, for        each agent of the agent set, an interaction parameter indicative        of a prediction of interaction of the corresponding agent with        the autonomous vehicle comprises:    -   predicting an interaction of the corresponding agent with the        autonomous vehicle; and    -   determining the interaction parameter based on the prediction.        Item 29. The non-transitory computer readable medium of Item 28,        wherein predicting the interaction of the corresponding agent        with the autonomous vehicle comprises:    -   determining, for each agent, one or more homotopy parameters        indicative of a constraint applied by the agent on a trajectory        of the autonomous vehicle; and    -   predicting the interaction based on the one or more homotopy        parameters.        Item 30. The non-transitory computer readable medium of Item 29,        wherein predicting the interaction based on the one or more        homotopy parameters comprises predicting the interaction by        inputting the one or more homotopy parameters or sensor data        into a neural network model.        Item 31. The non-transitory computer readable medium of Items        29-30, wherein the one or more homotopy parameters are        indicative of one or more of: a speed of the agent, an        acceleration of the agent, and a location of the agent.        Item 32. The non-transitory computer readable medium of Items        29-30, wherein the method comprises clustering, based on the one        or more homotopy parameters, agents of the plurality of agents.        Item 33. The non-transitory computer readable medium of any one        of Items 18-32, wherein an agent of the plurality of agents        comprises an object capable of a dynamic movement over time.        Item 34. The non-transitory computer readable medium of any one        of Items 18-32, wherein the method comprises: operating, based        on the trajectory, the autonomous vehicle.        Item 35. A system, comprising at least one processor; and at        least one memory storing instructions thereon that, when        executed by the at least one processor, cause the at least one        processor to:    -   obtain sensor data associated with an environment in which an        autonomous vehicle is operating;    -   determine, based on the sensor data, an agent set comprising a        plurality of agents located in the environment;    -   determine, for each agent of the agent set, an interaction        parameter indicative of a prediction of interaction of the        corresponding agent with the autonomous vehicle;    -   determine, based on the interaction parameters, a primary agent        set, wherein the primary agent set is a subset of the agent set;        and    -   generate, based on the primary agent set, a trajectory for the        autonomous vehicle.        Item 36. The system of Item 35, wherein to determine, based on        the interaction parameters, the primary agent set comprises to        determine the primary agent set by filtering out one or more        agents of the agent set based on the interaction parameters.        Item 37. The system of any one of Items 35-36, wherein to        determine, based on the interaction parameters, the primary        agent set comprises:    -   to determine the primary agent set by applying, to the agent        set, a prioritization scheme based on the interaction        parameters.        Item 38. The system of any one of Items 36-37, wherein the        filtering or the prioritization scheme is based on a criterion        applied to the interaction parameters.        Item 39. The system of any one of Items 35-38, wherein to        generate, based on the primary agent set, the trajectory for the        autonomous vehicle comprises applying a first model to an agent        of the primary agent set.        Item 40. The system of any one of Items 35-39, further        comprising to determine, by the at least one processor, based on        the interaction parameters, a secondary agent set, wherein the        primary agent set and the secondary agent set are mutually        exclusive subsets of the agent set.        Item 41. The system of Item 40, wherein to generate, based on        the primary agent set, the trajectory for the autonomous vehicle        comprises:    -   to generate, based on the primary agent set and the secondary        agent set, the trajectory for the autonomous vehicle.        Item 42. The system of any of Items 39-41, wherein to generate,        based on the primary agent set and the secondary agent set, a        trajectory for the autonomous vehicle comprises:    -   to apply a second model to one or more agents of the secondary        agent set.        Item 43. The system of Item 42, wherein the first model has a        higher fidelity than the second model.        Item 44. The system of any one of Items 42-43, wherein the first        model or second model comprises one or more of: an agent        recognition scheme, an agent prediction scheme, an agent        projection scheme, a trajectory extraction scheme and a        trajectory evaluation scheme.        Item 45. The system of any one of Items 35-44, wherein to        determine, by the at least one processor, for each agent of the        agent set, an interaction parameter indicative of a prediction        of interaction of the corresponding agent with the autonomous        vehicle comprises:    -   to predict an interaction of the corresponding agent with the        autonomous vehicle; and    -   to determine the interaction parameter based on the prediction.        Item 46. The system of Item 45, wherein to predict the        interaction of the corresponding agent with the autonomous        vehicle comprises:    -   to determine, for each agent, one or more homotopy parameters        indicative of a constraint applied by the agent on a trajectory        of the autonomous vehicle; and    -   to predict the interaction based on the one or more homotopy        parameters.        Item 47. The system of Item 46, wherein to predict the        interaction based on the one or more homotopy parameters        comprises predicting the interaction by inputting the one or        more homotopy parameters or sensor data into a neural network        model.        Item 48. The system of Items 45-47, wherein the one or more        homotopy parameters are indicative of one or more of: a speed of        the agent, an acceleration of the agent, and a location of the        agent.        Item 49. The system of any one of Items 45-47, wherein the        system causes the at least one processor to cluster, based on        the one or more homotopy parameters, agents of the plurality of        agents.        Item 50. The system of any one of Items 35-49, wherein an agent        of the plurality of agents comprises an object capable of a        dynamic movement over time.        Item 51. The system of any one of Items 35-50, wherein the        system causes the at least one processor to operate, based on        the trajectory, the autonomous vehicle.        Item 52. A method for operating an autonomous vehicle, the        method comprising:    -   obtaining, by at least one processor, sensor data associated        with an environment in which an autonomous vehicle is operating,    -   determining, by the at least one processor, based on the sensor        data, an agent set comprising a plurality of agents located in        the environment;    -   determining, by the at least one processor, a primary agent set        by applying, to the agent set, a prioritization scheme based on        the sensor data; and    -   generating, by the at least one processor, based on the primary        agent set, a trajectory for the autonomous vehicle.

What is claimed is:
 1. A method for operating an autonomous vehicle, themethod comprising: obtaining, by at least one processor, sensor dataassociated with an environment in which an autonomous vehicle isoperating; determining, by the at least one processor, based on thesensor data, an agent set comprising a plurality of agents located inthe environment; determining, by the at least one processor, for eachagent of the agent set, an interaction parameter indicative of aprediction of interaction of a corresponding agent with the autonomousvehicle; determining, by the at least one processor, based on theinteraction parameters, a primary agent set, wherein the primary agentset is a subset of the agent set; determining, by the at least oneprocessor, based on the interaction parameters, a secondary agent set,wherein the primary agent set and the secondary agent set are mutuallyexclusive subsets of the agent set; generating, by the at least oneprocessor, based on the primary agent set, a trajectory for theautonomous vehicle; and providing, based on the trajectory, control datato cause operation of the autonomous vehicle; wherein determining, basedon the interaction parameters, the primary agent set comprisesdetermining the primary agent set by discarding one or more agents ofthe agent set based on the interaction parameter.
 2. The method of claim1, wherein determining, based on the interaction parameters, the primaryagent set comprises: determining the primary agent set by applying, tothe agent set, a prioritization scheme based on the interactionparameters.
 3. The method of claim 2, wherein the discarding theprioritization scheme is based on a criterion applied to the interactionparameters.
 4. The method of claim 1, wherein generating, based on theprimary agent set, the trajectory for the autonomous vehicle comprisesapplying a first model to an agent of the primary agent set.
 5. Themethod of claim 4, wherein generating, based on the primary agent setand the secondary agent set, a trajectory for the autonomous vehiclecomprises: applying a second model to one or more agents of thesecondary agent set.
 6. The method of claim 5, wherein the first modelhas a higher fidelity than the second model.
 7. The method of claim 5,wherein the first model or second model comprises one or more of: anagent recognition scheme, an agent prediction scheme, an agentprojection scheme, a trajectory extraction scheme and a trajectoryevaluation scheme.
 8. The method of claim 1, wherein generating, basedon the primary agent set, the trajectory for the autonomous vehiclecomprises: generating, based on the primary agent set and the secondaryagent set, the trajectory for the autonomous vehicle.
 9. The method ofclaim 1, wherein determining, by the at least one processor, for eachagent of the agent set, an interaction parameter indicative of aprediction of interaction of the corresponding agent with the autonomousvehicle comprises: predicting an interaction of the corresponding agentwith the autonomous vehicle; and determining the interaction parameterbased on the prediction.
 10. The method of claim 9, wherein predictingthe interaction of the corresponding agent with the autonomous vehiclecomprises: determining, for each agent, one or more homotopy parametersindicative of a constraint applied by the agent on a trajectory of theautonomous vehicle; and predicting the interaction based on the one ormore homotopy parameters.
 11. The method of claim 10, wherein predictingthe interaction based on the one or more homotopy parameters comprisespredicting the interaction by inputting the one or more homotopyparameters or sensor data into a neural network model.
 12. The method ofclaim 10, wherein the one or more homotopy parameters are indicative ofone or more of: a speed of the agent, an acceleration of the agent, anda location of the agent.
 13. The method of claim 10, wherein the methodcomprises clustering, based on the one or more homotopy parameters,agents of the plurality of agents.
 14. The method of claim 1, wherein anagent of the plurality of agents comprises an object capable of adynamic movement over time.
 15. The method of claim 1, wherein themethod comprises: operating, based on the trajectory, the autonomousvehicle.
 16. A non-transitory computer readable medium comprisinginstructions stored thereon that, when executed by at least oneprocessor, cause the at least one processor to carry out operationscomprising: obtaining, by at least one processor, sensor data associatedwith an environment in which an autonomous vehicle is operating;determining, by the at least one processor, based on the sensor data, anagent set comprising a plurality of agents located in the environment;determining, by the at least one processor, for each agent of the agentset, an interaction parameter indicative of a prediction of interactionof the corresponding agent with the autonomous vehicle; clustering,based on the interaction parameter and a projected distance betweenagents of the agent set, a plurality of agents of the plurality ofagents into one or more clusters of agents; determining, by the at leastone processor, based on the interaction parameters and the one or moreclusters of agents, a primary agent set, wherein the primary agent setis a subset of the agent set; generating, by the at least one processor,based on the primary agent set, a trajectory for the autonomous vehicle;and providing, based on the trajectory, control data to cause operationof the autonomous vehicle.
 17. A system, comprising at least oneprocessor; and at least one memory storing instructions thereon that,when executed by the at least one processor, cause the at least oneprocessor to: obtain sensor data associated with an environment in whichan autonomous vehicle is operating; determine, based on the sensor data,an agent set comprising a plurality of agents located in theenvironment; determine, for each agent of the agent set, an interactionparameter indicative of a prediction of interaction of the correspondingagent with the autonomous vehicle; cluster, based on the interactionparameter and a projected distance between agents of the agent set, aplurality of agents of the plurality of agents into one or more clustersof agents; determine, based on the interaction parameters and the one ormore clusters of agents, a primary agent set, wherein the primary agentset is a subset of the agent set; generate, based on the primary agentset, a trajectory for the autonomous vehicle; and provide, based on thetrajectory, control data to cause operation of the autonomous vehicle.18. The system of claim 17, wherein to determine, based on theinteraction parameters, the primary agent set comprises to determine theprimary agent set by filtering out one or more agents of the agent setbased on the interaction parameters.
 19. The system of claim 17, whereinto determine, based on the interaction parameters, the primary agent setcomprises: to determine the primary agent set by applying, to the agentset, a prioritization scheme based on the interaction parameters. 20.The system of claim 17, wherein to generate, based on the primary agentset, the trajectory for the autonomous vehicle comprises applying afirst model to an agent of the primary agent set.
 21. The system ofclaim 17, further comprising to determine, by the at least oneprocessor, based on the interaction parameters, a secondary agent set,wherein the primary agent set and the secondary agent set are mutuallyexclusive subsets of the agent set.
 22. The system of claim 21, whereinto generate, based on the primary agent set, the trajectory for theautonomous vehicle comprises: to generate, based on the primary agentset and the secondary agent set, the trajectory for the autonomousvehicle.
 23. The system of claim 21, wherein to generate, based on theprimary agent set and the secondary agent set, a trajectory for theautonomous vehicle comprises: to apply a second model to one or moreagents of the secondary agent set.
 24. The system of claim 23, whereinthe first model has a higher fidelity than the second model.
 25. Thesystem of claim 17, wherein to determine, by the at least one processor,for each agent of the agent set, an interaction parameter indicative ofa prediction of interaction of the corresponding agent with theautonomous vehicle comprises: to predict an interaction of thecorresponding agent with the autonomous vehicle; and to determine theinteraction parameter based on the prediction.
 26. The system of claim25, wherein to predict the interaction of the corresponding agent withthe autonomous vehicle comprises: to determine, for each agent, one ormore homotopy parameters indicative of a constraint applied by the agenton a trajectory of the autonomous vehicle; and to predict theinteraction based on the one or more homotopy parameters.
 27. The systemof claim 17, wherein the system causes the at least one processor tooperate, based on the trajectory, the autonomous vehicle.
 28. The systemof claim 17, wherein to cluster the plurality of agents of the pluralityof agents into the one or more clusters of agents is based on theinteraction parameter, the project distance between agents of the agentset, a projected direction of each agent of the agent set, and aprojected speed of each agent of the agent set.
 29. A method foroperating an autonomous vehicle, the method comprising: obtaining, by atleast one processor, sensor data associated with an environment in whichan autonomous vehicle is operating, determining, by the at least oneprocessor, based on the sensor data, an agent set comprising a pluralityof agents located in the environment; determining, by the at least oneprocessor, a primary agent set by applying, to the agent set, afiltering discarding scheme based on the sensor data to discard one ormore agents from the agent set; determining, by the at least oneprocessor, based on the interaction parameters, a secondary agent set,wherein the primary agent set and the secondary agent set are mutuallyexclusive subsets of the agent set; generating, by the at least oneprocessor, based on the primary agent set, a trajectory for theautonomous vehicle; and providing, based on the trajectory, control datato cause operation of the autonomous vehicle.